Author Archives: Pangeanic

Protecting The Integrity Of Mental Health Documents In Translation

Translating medical health documents is part of the global language services industry that is worth more than $43 billion in 2017 and is expected to increase to $47.5 billion in 2027. Alas, translation is not a straightforward process. Protecting the integrity of mental health documents in translation has become a major concern for medical device companies and suppliers of translation services to healthcare companies. The main concern is that the integrity of information that is reworded might not reflect the original purpose of the documents.

The type of translation method that is employed is an important factor that determines the outcome of the interpreted material. There are several divergent views as to how mental health documents should be interpreted. One is the traditional back translation technique and the other is the usability method. Deciding which approach works the best is dependent on the target audience and purpose of the material. Pangeanic recently won accreditation to ISO 13485 for medical devices, a much coveted standard for suppliers of language services to the medical device industry.

icons showing heads depicting Mental Health - emotional intelligence - conflict - obsession - Personal development



Back translation and its benefits

All over the world, racial demographics are changing. A growing and racially diverse population implies that more than one language is spoken. Because of this situation, the need for interpretation and translation services has increased. In the US, the job outlook for interpreters and translators is estimated to grow to 18% from 2016-2016.  While it is never too late to learn another tongue, the language barrier might become an impediment for many people to access mental health services missing out on valuable opportunities for diagnosis and treatment.

Mental health documents are very specific and require knowledge of the terms used in the field. Using back translation for papers that pertain to mental health assures their integrity so that the exact meaning is conveyed. For researchers and health care workers including physicians, nurses, psychologists and support staff in the medical field, back-translated documents are manageable and understandable. However, for a patient, getting a highly technical document translated from a source language into the target language might not be comprehensible. Technical terms are difficult to understand and have little to no meaning to them.

Translating documents based on usability

Bilingual doctors and health care workers are not always available to interpret for patients. In situations where patients speak a different language, informational materials on mental health is the closest thing to direct translation.  The use of the skopos theory in translation works by focusing on the source text and its purpose. Its main advantage is that it conveys the exact purpose of the original text in the way that users would want to use it. In this case, patients would find it easy to digest mental health materials if they were translated with them in mind.
translating mental healthcare documents mac view
As an example, mental health pamphlets can be interpreted in an understandable manner supplemented by infographics. Documents to be filled in requesting for tests and exams can also benefit from this type of translation.

Translated mental health documents are useful when the end users are identified. The use of several techniques to translate documents offers advantages to different audiences. For patients who are not able to speak in another language other than their own, getting documents that serve their purpose and for which they are supposed to be used is the better option.

The Future of Machine Translation

It’s an exciting time for translators indeed, with December 2017 seeing the launch of two AI systems able to teach themselves any language. According to Global Market Insights, the translation industry will have a $1.5 billion net worth by 2024, with “the ability to translate different languages according to customer preferences and the lack of existing translator for several specialized fields and language combinations providing tremendous growth opportunities for the industry.” This is good news both for individual translators and translator tech developers. Both will be called upon to deliver increasingly specialized translation solutions to fulfil an ever-growing global demand.

Technology does not Aim to Replace Translators

Before delving into the machine translation systems that will be dominating the industry, it is important to understand that these systems are not meant to replace human translators. For example, Korea has one of the highest robot populations in the world and hardly 0 unemployment. Higher automation creates more “human” and better cognitive jobs. Translation continues to be an attractive profession that is directly in line with the millennial demand for online-based income sources. The annual mean wage for this profession stands at around $45,910, though salaries can be considerably higher. The spirit behind new technology is one of co-existence and of easing the burden of translators working on large volumes.

The Global Machine Translation Market – Forecasts from 2018 to 2023 Report

This report delves into market dynamics, global machine translation, and competitive intelligence. It identifies four major technologies that will be influencing the global machine translation market: statistical machine translation, rule-based machine translation, hybrid machine translation, and neural machine translation. Its authors note that “The major factors driving the market are the growing volume of big data, the need for cost effective translation, and increasing online content. Moreover, globalization is raising the demand for location based content across various industry verticals such as electronics, travel, e-commerce and hospitality.”


Neural Machine Translation (NMT)

This is the latest approach in industry and the closest related to artificial intelligence. It reduces the post-editing effort thanks to its focus on fluency. In many ways, it is an “ultra-statistical” approach, with several layers processing the information and nodes passing on information and ensuring accuracy. It has been called neural as the way layers and nodes work resembles very much the way neurons work in humans (and mammals in general). There are three interesting features that Neural Machine Translation can offer. Some of its features help it understand the similarities between words, analyze entire sentences, and evaluates the fluency of a sentence in the target language by analyzing a few words at a time. NMT is just one of many exciting ways to elicit more natural, fluent sounding translations in the target language. In fact, academia is still struggling to understand what happens inside the “black box” once the neural network starts to train. The “conclusions” are pretty amazing and sometimes surprisingly accurate when it comes to human language processing because of the number of calculations, weights, gradients applied and attention models, even between non-related languages and languages with a rich morphology.

Further information: Machine Translation Services


Statistical Machine Translation

This system employs a learning algorithm to a large body of previously translated text, using this algorithm to translate texts that have not been encountered before. The system is suitable to documents that focus on one particular subject. Its advantages include the plethora of existent current platforms and algorithms (which make the system cheap and quick) and the fact that it requires little space (i.e. does not need a server of its own). Training is done in CPU servers and it is easily deployed. Decoding is also fast and it serves pretty much as large “translation memory” that places n-grams (groups of words) next to each other. Most systems we are familiar with were statistical until the end of 2016, when Google published its paper on neural machine translation. Its weak point is that it works in a specific context and is not adept at colloquial language and idioms and also it works better with close languages but it does not perform well syntactic re-ordering (as it would be required when translating from English into German or Japanese).

Rule-Based Machine Translation

This is the first system developed in the world of machine translation and it dates from the 1950’s; it relies on the rules of grammar, lexicon, and software programs to process rules. Translation occurs by pattern matching rules. The key attribute of this system is that it permits the avoidance of matching unfruitful rules. The strong point of rule-based translation is its ability to deeply analyze language at the semantic and syntactic levels. Its weak point is the large numbers of rules that govern each language which may end up contradicting each other and the “mechanic” sound of it some times.

Hybrid Machine Translation

This method uses multiple machine translation approaches within one system. There are many types of hybrid machine translation. One popular approach combines a statistical engine with a rules-based approach (both at the pre- and post-processing stage). The system offers great flexibility, control, and precision, and differs from a purely statistical-based approach. Historically, it aimed at adding the best of rule-based MT (syntactic information, that is “understanding” of collocation and where words should go, their proper declension in the case of morphologically-rich languages, re-ordering, etc.) combined with the power of data-driven statistical MT.

Machine translation is not aimed at replacing human translators, but rather, at facilitating speed and precision. It creates a market for fast translation where humans cannot translate massive amounts of data very quickly. It can also be used for eDiscovery, for example. Machine Translation can also help reduce the time spent by editors on proofing and correction, owing to increasingly sophisticated algorithms and ways of analyzing words and sentences at a detailed level.  In fact, many are arguing that the role post-editors will soon develop  and transform into that of a “reviewer” as the quality of the neural output is so good.

There is no doubt that 2018 is already proving to be an exciting time for the AI age.

Disintermediation – The Uber of Translation and iADAATPA open source multi-MT platform

by Manuel Herranz

Speaking at two conferences in two very different scenarios and to two very different audiences gives you the precious opportunity to get a taste of what the market thinks, the fears and the wishes. By market I mean people that represent business, companies or represent themselves but they have an influence on what others think in their profession. In my case, this is happened in Athens at the Elia Together conference and at Gala Boston recently. Although the presentations were targeted at two very different groups, they shared some common ground. Both audiences contained professional translators and linguists and representatives of translation companies. The presentation in Athens dealt more with the future of translation as a profession, some marketing development tips and a short summary of our iADAATPA open source multi-MT platform project which inevitably led to the question of “How does neural machine translation work”?. The presentation in Boston was solely dedicated at the iADAATPA platform and how it will become an open source MT platform with ready-tested plugins and APIs to CAT tools and MT services, with a little overlap on how does neural machine translation work. Let me share with you a few things I learnt from talking to professional translators and translation business over the last two months.

Elia Together Athens

The Uber of translation

Full house. More than 100 people sat to take away some lessons about how the business model for medium and small translation agencies must change (well, even for the model of the large ones) and embrace B2C. It’s not that the typical cascade buying and selling will disappear, but many companies and even freelancers have not realized yet how many times people use Google nowadays to search for services… including translation services. And it doesn’t matter if you are in Boston, Athens, Osaka, Guadalajara City or Lyon. Well-delivered services can be anywhere as long as they can deliver what they promise. Look at Amazon. I hammered home the idea that few translation companies actually do what they say they do (translate, for example their own website in 10 languages). They don’t speed and solve communication problems (they actually create a layer of administrative work). Who will be the Uber of the Translation Industry - a tweet by Marina Oreskovic The point about disintermediation seemed to be interesting more for companies than for freelancers. As large text loads head towards higher quality neural machine translation (which begins to be almost indistinguishable from humans), the idea of microservices and hourly rates begins to settle in the minds of translation managers and translators alike. A mixed audience as it was (half freelancers, half companies), I was surprised to see the interest in iADAATPA open source multi-MT platform that will not only provide free machine translation to public administrations via eTranslation from the European Commission, but will also be free to for use as an multi-MT vendor platform by private individual and industry. Having a single hub where all your connectors and APIs are already in place so you can use best performing engines in domains and language pairs from several MT vendors is attractive not only for the European Union. It is attractive to anybody that would like an on-premise machine translation platform. In 2006, Google ditched a ruled-based system (Systran) to develop its own platform. In 2010 the first statistical machine translation with re-training features was released as PangeaMT. Many followed, mostly as SaaS and cloud services. But no full MT management platform has ever become open source. APIs and connectors have remained fiercely proprietary. iADAATPA will make MT much easier to run and change from one provider to another, even at company level.

A version of my presentation can be seen in slideshare
Disintermediation presentation Elia together Athens 2018 Manuel Herranz

Gala Boston 2018

The Language of Business. The Business of Language

Language conferences are a thing to see. They really are different to most types of conferences: the interaction, the almost incestuous in-selling… you don’t see rookies, middle management or small practices in tool machine exhibitions, life sciences conferences, not even in toy and fair exhibitions. At language conferences you can speak to a tool developer who doubles up in the executive board, representatives of think tanks and partners in a small business. My talk this time was for a smaller audience and more focused on iADDATPA as an EU project (part of the Connecting Europe Facility initiative). I had kept some common slides from Greece to briefly explain how neural machine translation works but I didn’t get there practically. I invited the audience to ask any time as I introduced the platform and a very inquisitive audience did just that. So no more than 7 minutes into the presentation, the audience was asking the right questions: “When will be able to get our hands on it?” “Is this the beginning of a dynamic benchmark?” “A real marketplace of machine translation where the system decides the best system producing a paragraph, or a sentence?” “How will you decide intelligently one system is better than the other?” “What happens if you re-train?” “Will it be able to re-train own engines?” “Can iADAATPA be deployed on-premise?”. All I could think is that audiences, or the market, is very eager not just to adopt the technology in passive mode, but to own it, play with it, customize it and be able to run its own machine translation hub for its clients. Linking up to the ELIA presentation in Athens, I sense the savvy business have already realized that there is a huge change in the business model in the pipeline and that MT is not the realm of the top ten companies in the world or large multinational organizations. It’s likely to become the CAT tool and the service everyone provides in the near future. I couldn’t attend all talks, but I’d like to summarize a very worthy initiative: TAPICC, from Gala itself. Currently API landscape is a wild west with a lot of unnecessary variation and a continuous reinvention of the wheel, with a lot of wasted money for clients, LSPs and tool vendors, leading to a loss of operational freedom. The aim of TAPICC is to unify the API scenario, working with industry, academics, in a similar vein as iADAATPA is doing by bringing together several machine translation players offering a single access point and common APIs to connect to many CAT tools and MT deployment scenarios. A picture of Gala attendees TAPICC has created several working groups and agreed upon uses and a useful framework for metadata, use cases, best practices, and classes, so it is now looking for quickly implementable classes and use cases and iADAATPA can well be one of them by deploying its standard as a single access point for machine translation services. Thus,  it will reduce the cost of integration and will allow for quickly onboard new clients and systems, LSPs, etc., finally easily embedding L10N in content processes and enterprises. A 2nd objective of TAPICC is the interaction between different systems so that 2 TMs tools can talk to each other. This is happening already on a proprietary level and they are all different and TAPICC wants to support everything. A good use scenario, for example, can be the semantic enrichment of units, terminology, TM and MT for example in an xliff file that asks for terminology results and with a layout that is “good enough”. In one word, it can be as simple as having a file that needs to be sent to a machine translation engine but keeping the same format. As TAPICC is already looking for use cases, the API specification can start to be tested and shaped during development stage.

A version of this presentation can also be seen in slideshare.

iADAATPA open source multi-MT presentation at GALA Boston

Towards a multilingual world: the future of the English language in the 21st Century

by Carolina Herranz-Carr 

Carolina is an Account Executive at East Creative Agency, a voice-over artist in English and Spanish and a graduate from Brunel University. Her passion for languages comes from her bilingual background.

To share a common language and a cultural aptitude are well-known determinants of trade, a belief long held by successful businesses in their international marketing campaigns and trade negotiations. With the far-reaching postcolonial spread of the English language and US dominance in the Western hemisphere, international trade, global politics and cultural interactions soon adopted English as the common ‘lingua franca’, whilst Russian dominated Eastern Europe and French most of Western Africa. This phenomenon accelerated the globalised world of today and created several cultural spheres of influence where none existed before and strengthening older links in some cases. However, in the face of an unsettled post-Brexit anxiety, a perceived void in US global leadership, and China’s entering in the big league of global markets, many are beginning to contemplate the socio-economic consequences of this global shift in direction. What will be the impact on culture, language and business if China is to move towards the centre stage in the world?

A multilingual world and the English language

China’s narrative, lead by Xi Jinping sets out a vision for the years ahead; In his speech, at the 19th National Congress of the Chinese Communist Party, he pronounced a “new era” for China. Pledging for further liberalisation of its markets (while simultaneously calling for stronger state firms). China’s confirmation for its commitment to free trade and its insistence that it mustn’t “hide its light under a bushel and be a modest player abroad”, could mean a golden age for globalisation; generating new opportunities for business and trade. However, China’s integration into the international economy is built upon a socialist market, and carries with it a considerably different cultural approach in comparison to its Western trade partners. The importance of mutual understanding, linguistic and cultural connection will be the key drivers of a successful 21st Century. Additionally, as China’s presence in the global stage rises, a potential popularity of the Chinese language abroad could also be on the increase. Spanish, Portuguese French and the English language grew from their home basis in Europe as a result of commercial expansion from the 15th century onwards.

An initiative carried out by the National Security Education Program (NSEP) at the U.S. Department of Defence, highlighted the importance of language skills for international business. Within their Language Flagship report, they noted how the state of Washington claimed to a loss of revenues due to inefficient translation of training contracts and curricular materials into Chinese and other languages. The NSEP concluded that, the preclusion of revenues and relationships that derived from mistranslation, should be countered by cultural awareness within businesses, and an increase in a linguistically skilled workforce. This idea could prove exceptionally important for Europe and Asia in the years ahead, where an increase in trade is expected from Mr. Xi’s ambitious $900 billion New Silk Road Initiative; involving a hefty investment on infrastructure in more than 60 countries. If successful, this activity would centre Eurasian trade on China. Likewise, a post-Brexit UK could also see a shift in trade intensity with China in an effort to connect with fresh markets overseas. If China and the UK sign a FTA, a rise in competition between the EU and Britain could further shape China’s role in the world.

As the divorce date looms closer, the UK must consider the fate of one of its essential, perhaps overlooked assets; the English language as an economic resource (see more in our “The demise of English as an international language?“). Britain has long enjoyed the lucrative benefits of its language dominating the vocabulary of diplomacy, cross-cultural communication and trade, leaving no real incentive to learn another language. However, the possibility of an EU without Britain has left many reassessing the relevance of English as an official language within the union. In the Republic of Ireland, the official language is Gaelic and not English and in Malta, where the majority of people speak English after a period of colonisation, the native language remains Maltese; the only Semitic language spoken in Europe. Some speculate there will be an increase of pressure on EPSO, the EU’s recruitment agency, to discontinue English as one of the three required languages, leaving French and German as likely substitutes. 

Brexit - United Kingdom and European Union

Nonetheless, while the replacement of the English as the cross-cultural communication of choice may be too bold an assumption; with 38% of Europeans currently speaking English as a second language, it is conceivable to acknowledge that China’s economic rise could translate into more Mandarin speakers across the globe. Thus, we may slowly be progressing towards a multilingual world where the English language can play a role, but not the dominant role. In order to prosper and secure economic stability, Britain should not solely rely on its own language to nurture the development and advancement of trade overseas. As stated on the GOV official website, UK business “should not assume Chinese firms will have English-speaking staff”. Alternatively, a strong understanding of the economic, linguistic and cultural differences are crucial if the UK pretends to connect with foreign markets.

Both Brexit and Trumps-lead US, have provoked food for thought about the future political landscape, leading many to ponder upon their own role in the affairs. Canada’s Prime Minister Justin Trudeau regarded Canada’s significant allies and trading partners the U.S. and the U.K. as “turning inward”, urging Canada should take advantage of their isolated approach to seize new opportunities abroad. In the same vein, Mr. Xi championed an “enlightened new socialism” amid “crises and chaos” in Western liberal democracies. The future of trade and business will depend on mutual trust, improved language skills in business and a strong understanding of China’s cultural and political landscape as it prepares to further open its markets. Without linguistic connection through common language, efficient translation of services and a cultivation of strategic language policies, the barriers of mistranslation could gradually take their toll.

Our Nordic Translation Industry Forum Blog diary!

by Garth Hedenskog

From Wednesday, the 22nd until Friday the 24th of November, Pangeanic traveled north to Helsinki to attend our first Nordic Translation Industry Forum! And what an amazing event it was. View of Helsinki City Center in the early evening with tram Let’s start off by saying that Helsinki is a stunning location for business or pleasure. The team was greeted by light snow and a high of 1ºC for most of the 4 day stay! Garth Hedenskog (our sales director) and Alex Helle (our chief research and developer) were lucky enough to go this year. This was naturally a very popular event/destination to attend with a lot of staff at Pangeanic very eager to go! Here is Garth and Alex trying to look busy with at the interpreting challenge, they didn’t fool anyone! Garth Hedenskog and Alex Helle Garth and Alex of course didn’t just go for the beautiful scenery, adventure and crisp fresh Nordic air, they went to showcase Pangeanic’s technology, see what the Nordic translation industry had to offer and of course let their hair down and mingle with our amazing localization colleagues. And this is how the 3 days unfolded in their own words….. The reception was apparently wonderful but our flight unfortunately got in a little late so missed the whole thing! Chatting with our colleagues the next morning, we were sorry to have missed it! Day 1: We got in nice and early to setup our stand. The people at NTIF we great and made the whole experience truly unforgettable. Everything functioned like clockwork and all support and assistance made the whole setup a really simple and fun experience.Pangea Machine Translation banner Throughout day 1 we were treated to some really inspiration and unforgettable speeches and here were some of them… Klaus Fleischman from Kaleidoscope spoke about the power of words or how we never get a second chance to make a first impression Gábor Bessenyei, MorphoLogic Localisation addressed how custom neural MT engines are at your fingertips Lara Millmow, from Elia gave an interesting presentation about taking a broad view on your business Ádám Marjai, memoQ spoke about their Translation project management solution We were treated to many other very interesting presentations. That night we were treated to an exquisite dinner followed by some questionable dancing! We had Reindeer for dinner, first time for many. As they say, when in Rome… The location was absolutely beautiful, right near the water of the Baltic sea. We had a scrumptious 3 course meal, drinks, dancing and fascinating conversations. What more could we ask for. Alex Helle, Garth Hedenskog and GALA Board Director Tea Tea C Dietterich The night absolutely flew by which no doubt proved how successful and fun it was. Day 2: Day 2 started with our gracious hosts laying out a spread of healthy juices, snacks (there may or may not have been miniature bottles of vodka available) and most importantly some headache tablets for the weary party animals. Alex and I were of course fresh as daisies (kind of)…. Day 2 was also sadly our last day so we really tried to speak to all the visitors and exhibitors we hadn’t had time to meet with during the previous day. Our neighbors (Memsource) had one of those virtual reality games which was a great energy boost when we needed it! Thanks Memsource! Again the talent presenting was incredible so we snuck away as often as we could to learn as much as possible about trends and best practices.

With Jaba Translations CEO Joaquim Alves

With Jaba Translations CEO Joaquim Alves

Again, some of the standout presentations for us were: Salvo Giammarresi from PayPal explaining what Globalization is and how LSP (traditionally Language Service Provider) should strive to be more Language Service Partners. We were amazed by the talent and expertise on show at the Interpreting Software Challenge. It was really inspiration stuff and the company’s founders came from some really disadvantages backgrounds. I’d like to mention them all and encourage you to check their companies out. Tulka Interprefy Interactio Tikktalk Youpret Kudo We had a delicious lunch kindly sponsored by AAC Global where we had an opportunity to see some amazing friends. 20171124_132314 Our booth was very busy with students and LSPs learning about the latest developments in the Neural Machine Translation field, in particular about Pangeanic’s PangeaMT solution. We demonstrated how it is completely compatible with our centralized translation memory system – ActivaTM.  Cor (our web based translation management system with built in crawler) was also very popular and we couldn’t have been happier telling everyone who would listen all about it. We could’ve easily stayed another few days but we sadly had to pack up that afternoon at about 5pm but not before we were involved with the prize giving and farewells where we gave an amazing Fitbit Alta HR away. The lucky winner was …..drum roll please….. Karel Mostek from Moravia! Thank you Helsinki and NTIF, it was amazing! We look forward to being back next year!

french bulldog with French beret hat and French flag behind a laptop

How can a global brand be more local? 3 tips to reach more customers

If you have been working hard to develop and set up an eCommerce site, you know that is the first step in a long journey. Now you need clients to find your site. Adjusting your eCommerce site to the feel and look of local markets is essential because it reassures customers that you have paid enough time and consideration for your brand to be more local to them. You are considering their language, their local customs and traditions.

Three things to think about:

1) Not all images convey the same message

The majority of eCommerce sites are all about the visual and the price. Planning a little bit about images that will not fit the palate of some audiences is paramount. Think about the right kind of images for your eCommerce website. For example: high-quality images for your products are neutral and will often come for the product manufacturers themselves. However, if you are using your own images for your products, our recommendation as internationalization experts is to give them a little thought and see if they are appropriate for the countries and markets you are addressing. eCommerce sites sell everything, from books to furniture, drinks and even translation services… But fashion sites tend to be very popular and probably the most obvious example. Time after time, winners are the ones who carefully select and showcase their products because local markets react positively or negatively to whatever you put in front of them. cartoon lady in short jeansClearly the sense of fashion is different from country to country. Dress codes can be radically different from one culture to another. Ties are popular in the Western world, China, Japan and Korea, but there are differences in use. Some items can be plainly offensive to some local customers. Tight-fitting clothes are not the thing to market in India, not above-the-knee short jeans or low necklines in Morocco, Algeria, Egypt.

If you are using your own images for your products, our recommendation as internationalization experts is to give them a little thought and see if they are appropriate for the countries and markets you are addressing.

The Middle East is experiencing a great burst in eCommerce. The eCommerce sector in MENA (Middle East and North African countries) reached US$10 billion mark in 2016 and it is ‘to grow tenfold’ by 2020. A pan-Arab government body headquartered in Cairo is preparing to release a five-year strategy. Comprising representatives from 14 governments across the MENA region, it claims the region’s e-commerce sector will leap from $20 billion in 2017 to $200 billion beyond 2020 – and international fashion brands who feature images which offend customers on religious or cultural grounds will fail.

2) Colors Your website design should also take into account that the choice of colors affects the subconscious when visitors first land on your site. Take red for instance: the color of passion in most Western countries, red is associated with mourning in South Africa. The section of red in the country’s flag symbolizes violence and sacrifices that were made during the struggle for independence.

Shanghai Temple

Shanghai Temple

In China, red represents celebration, happiness. It is everywhere in China: buildings, roofs, clothes, websites. The Chinese flag is red. Ask any Chinese translator about red: it is meant to bring luck, prosperity, happiness, and a long life to the people. And how about yellow? No bullfighter will dare to come out dressed in yellow in Spain and Latin American countries. It brings bad luck! In China and Japan, yellow is associated with direty sex or pornography. Chinese and Japanese use the term “yellow film”, “yellow joke” or “yellow book” meaning pornographic films, jokes or books.

Spaniards would call them “green”! In France yellow stands for jealously, betrayal, weakness, and contradiction. In the Middle Ages, people painted the doors of traitors and criminals yellow. Yellow is reserved only to people of high rank in many African nations, because it is easily associated to gold, and gold is money, quality, success. And in Germany, yellow symbolizes jealousy. Want to know more? If you are reading this blog from the US or Europe, you will agree blue is a strange color. It is the traditional color for boys (pink for girls). It is also a color that stands for trust, security, and authority (many conservative parties use light shades of blue) as well as banks and institutions (Citibank and Bank of America). In China, blue is considered a feminine color. In Judaism, blue is the shade for holiness and divinity (the Virgin Mary is depicted in blue in Catholic countries).

In Hinduism, blue is the color of Krishna—the most highly worshipped Hindu god who embodies love and joy, and destroys pain and sin. Talking about pink, this color translates as “foreign color” in Chinese as it was unrecognized until it emerged into the culture due to increasing Western influences. Orange has almost positive feelings everywhere: in many Western cultures, orange is considered a fun and edgy color, and represents curiosity, a thirst for the new, for creativity. In Japan and China, orange is also positive, being linked to good health, courage, happiness and love. And in India, it’s symbolic of fire. The orange-colored spice, saffron, is considered to be lucky and sacred. Only in many Middle Eastern countries, such as Egypt, orange is associated with mourning.

3) Logo and Tagline

We would recommend changing the logo unless there was something terribly wrong in a particular country or culture. The tagline is something else. It may be culturally attached to the country it comes from. We have dealt with some mistakes in that kind of translation which have become part of the translation folklore. Follow this link to read and have a good time. Translations are the most cost-effective way to help your website appear in the search result pages of international markets. Translating an eCommerce is the way not to depend on a single, home market.  73% of customer prefer to buy goods in their local language. eCommerce customers move quickly (at the click of a button!) when it comes to deciding if they will buy from a website. But please remember, a properly localized website makes life a whole lot easier to beat the competition.

The Pangeanic neural translation project

The last few months have been extraordinarily busy at Pangeanic, with a focus on the application neural networks for machine translation (neural machine translation) with tests into 7 languages (Japanese, Russian, Portuguese, French, Italian, German, Spanish), the completion of a national R&D project (Cor technology as a platform for translation companies offering an integrated way of analyzing and managing website translation and document analysis), the integration of CAT-agnostic translation memory system ActivaTM into Cor and our neural engines, and the award by the European Union’s CEF (Connecting Europe Facility) of the largest digital infrastructure project to build secure connectors to commercial MT vendors and the EU’s own machine translation service (MT@EC) for public administrations across Europe. Leading machine translation developers such as KantanMT, Prompsit, Tilde and our PangeaMT join forces with consulting company Everis to build IADAATPA, a system that will intelligently work on domain adaptation and the selection of the most appropriate engines through secure connectors for Public Administrations in the EU.

So, time to recap and describe our experience with neural machine translation and how Pangeanic has decided to shift all its efforts into neural networks and leave the statistical approach as a support technology for hybridization.

The Pangeanic neural translation project

We selected training sets from our SMT engines as clean data to train the same engines with the same data and run parallel human evaluation between the output of each system (existing statistical machine translation engines) and the new engines produced by neural systems. We are aware that if data cleaning was very important in a statistical system, it is even more so with neural networks. We could not add additional material because we wanted to be certain that we were comparing exactly the same data but trained with two different approaches.

A small percentage of bad or dirty data can have a detrimental effect on SMT systems, but if it is small enough, statistics will take care of it and won’t let it feed through the system (although it can also have a far worse side effect, which is lowering statistics all over certain n-grams).

Visual sample of statistical candidates with best candidate proposed in a statistical machine translation system

Visual sample of statistical candidates with best candidate proposed in a statistical machine translation system

We selected the same training data for languages which we knew were performing very well in SMT (French, Spanish, Portuguese) as well as those that have been known to researchers and practitioners as “the hard lot”: Russian as the example of a very rich morphologically language and Japanese as a language with a radically different grammatical structure where re-ordering (that’s what hybrid systems have done) has proven to be the only way to improve.

Japanese neural translation tests

Let’s concentrate first with the neural translation results in Japanese as they represent the quantum leap in machine translation we all have been waiting for. These results were presented at TAUS Tokyo last April. (See our previous post TAUS Tokyo Summit: improvements in neural machine translation in Japanese are real).

Japanese neural translation engine for the electronics and IT field

Tokenizer.perl and Mecab were used for English and Japanese tokenization respectively.

We used a large training corpus of 4.6 million sentences (that is nearly 60 million running words in English and 76 million in Japanese). In vocabulary terms, that meant 491,600 English words and 283,800 character-words in Japanese. Yes, our brains are able to “compute” all that much and even more, if we add all types of conjugations, verb tenses, cases, etc. For testing purposes, we did what is supposed to do not to inflate percentage scores and took out 2,000 sentences before training started. This is a standard in all customization – a small sample is taken out so the engine that is generated translates what is likely to encounter. Any developer including the test corpus in the training set is likely to achieve very high scores (and will boast about it). But BLEU scores have always been about checking domain engines within MT systems, not across systems (among other things because the training sets have always been different so a corpus containing many repetitions or the same or similar sentences will obviously produce higher scores). We also made sure that no sentences were repeated and even similar sentences had been stripped out of the training corpus in order to achieve as much variety as possible. This may produce lower scores compared to other systems, but the results are cleaner and progress can be monitored very easily. This has been the way in academic competitions and has ensured good-quality engines over the years.

The standard automatic metric in SMT did not detect much difference between the output in NMT and the output in SMT.

BLEU does not detect the huge difference in perceived quality - WER is a better indicator

BLEU does not detect the huge difference in perceived quality – WER is a better indicator

However, WER was showing a new and distinct tendency.

NMT versus SMT results in Japanese

NMT shows better results in longer sentences in Japanese. SMT seems to be more certain in shorter sentences (training a 5 n-gram system)

And this new distinct tendency is what we picked up when the output was evaluated by human linguists. We used Japanese LSP Business Interactive Japan to rank the output from a conservative point of view, from A to D, A being human quality translation, B a very good output that only requires a very small percentage of post-editing, C an average output where some meaning can be extracted but serious post-editing is required and D a very low quality translation without no meaning. Interestingly, our trained statistical MT systems performed better than the neural systems in sentences shorter than 10 words. We can assume that statistical systems are more certain in these cases when they are only dealing with simple sentences with enough n-grams giving evidence of a good matching pattern.

We created an Excel sheet (below) for human evaluators with the original English to the left and the reference translation. The neural translation followed. Two columns were provided for the ranking and then the statistical output was provided.

A table showing original English and Japanese reference translation

Neural-SMT ENJP ranking comparison showing the original English and the reference translation, with the neural ranking to the left and the statistical system to the right

German, French, Spanish, Portuguese and Russian neural translation results

The shocking improvement came from the human evaluators themselves. The trend pointed to 90% of sentences being classed as perfect translations (naturally flowing) or B (containing all the meaning, with only minor post-editing required). The shift is remarkable in all language pairs, including Japanese, moving from an “OK experience” to a remarkable acceptance. In fact, only 6% of sentences were classed as a D (“incomprehensible / unintelligible”) in Russian, 1% in French and 2% in German. Portuguese was independently evaluated by translation company Jaba Translations.

Human evaluation of neural translation in German, French, Russian

Human evaluation of neural translation in German, French, Spanish, Portuguese, Italian, Russian

This trend is not particular to Pangeanic only. Several presenters at TAUS Tokyo pointed to ratings around 90% for Japanese using off-the-shelf neural systems compared to carefully crafted hybrid systems. Systran, for one, confirmed that they are focusing only in neural research/artificial intelligence and throwing away years of rule-based work, statistical and hybrid efforts.


Systran’s position is meritorious and very forward thinking. Current papers and some MT providers still resist the fact that despite all the work we have done over the years, Multimodal Pattern Recognition has got the better hand. It was only computing power and the use of GPUs for training that was holding it behind. The above article at PangeaMT provides some information about what is changing in the automated translation landscape as we speak and an example of the first neural papers back in the 90’s which has guided much of our own R&D.

Neural networks: Are we heading towards the embedment of artificial intelligence in the translation business?

BLEU may be not the best indication of what is happening to the new neural machine translation systems, but it is an indicator. We were aware of other experiments and results by other companies pointing in a similar direction. Still, although the initial results may have made us think that there was no use to it, BLEU is a useful indicator – and in any case, it was always an indicator of an engine’s behavior not a true measure of an overall system versus another.  (See the wikipedia article

Machine translation companies and developers face a dilemma as they have to do without the research, connectors, plugins and automatic measuring techniques and build new ones. Building connectors and plugins is not so difficult. Changing the core from Moses to a neural system is another matter. NMT is produces amazing translations, but it is still pretty much a black box. Our results show that some kind of hybrid system using the best features of a SMT system is highly desirable and academic research is moving in that direction already – as it happened with SMT itself some years ago.

I brought some useful tips from my attendance to SlatorCon in London. One is that translation buyers are still in sheer need of affordable translation solutions that can centralize assets and workflows. Another one is that neural MT is taking center stage as the technology that can truly change the game. The most important one, I would say is that venture capital money is pouring into the translation industry because it sees strong similarities with other industries (advertising, for one) that were disrupted years ago and produced something new.

“There was not a lot of technical innovation in the advertising industry until the late 1990s,” observed Marcus Polke, Investment Director from Acton Capital Partners. “And then came the Internet, which bypassed and marginalized ad agencies as online and offline advertising transformed into a complex landscape.

Yes, the translation industry is at the peak of the neural networks hype. But looking at the whole picture and how artificial intelligence (pattern recognition) is being applied in several other areas, in order to produce intelligent reports, tendencies and data, NMT is here to stay – and it will change the game for many, as more content needs to be produced cheaply with post-edition, at light speed when good machine translation is good enough. Amazon and Aliexpress are not investing millions in MT for nothing – they want to reach people in their language with a high degree of accuracy and at a speed human translators cannot.

TAUS Tokyo Summit: improvements in neural machine translation in Japanese are real

Not that business plans are written in stone any longer, but efforts to provide an insight by experts are always welcome. TAUS Tokyo Summit provided a much awaited for set of good news about perceived human translation improvements in neural machine translation in Japanese. English-Japanese was a well-known difficult language pair for rule-based machine translation and statistical machine translation provided a really awful experience for many Japanese audiences. It has historically been one of the hardest language combinations to automate. It seems that neural machine translation may be the answer.

Day 1 – Where is the translation industry heading?

Jaap began by summarizing the latest meeting of thought leaders in Amsterdam who met in Amsterdam in order to brainstorm a potential landscape and priorities for the language industry in the five years. If machine translation hype was at its peak five years ago with statistical machine translation and all sort of hybrids, we are now beginning to experience the neural MT hype. But adopters and developers are much wiser. If data was king some years ago, it seems we may not need so much in the future. Datafication was a process started some years ago after an article called “The Unreasonable Effectiveness of Data” (Elon Halevy, Peter Norving, Fernando Pereira, 2010, Google). The article said that the more data the better if our aim was to collect data to train machine translation engines and models. The more data we had to teach the algorithms decide what was best, the better a statistical system would translate. The problem has always been the unclarity about copyright issues with translation data. For example, law is different between US and Europe with regards to translation ownership.

TAUS has been focusing in the development of tools and practical services to the translation industry it serves, such as

  • Machine Learning
  • Quality Dashboard
  • Machine Translation
  • Intelligent TM
  • Interoperability, etc.

The set of services and tools (such as DQF) may soon become industry standards and they can be used to benchmark and measure productivity in-house and also with other (anonymized) players. DQF is now available as an API and can collect data real time as translators work, without disturb them. It is a transparent model and reports can be tracked to track reports, statistics and benchmark against other translators.

Jaap mentioned that Europeans are very worried that Google and Microsoft to “fix the problem” and be left out of the language technology race, referring to one of his previous articles “The Brains but not the Guts”. Europe is exporting talent to the US, an army of language scientists who are helping those two giants overcome the language barrier. On the other hand, machine translation has been accepted, it is becoming an API. On a daily basis, output from machines is 500 times bigger than the output from all professional translators put together. The translation industry is growing but also changing radically. What companies do nowadays is not pure translation any longer but telemanagement, post-editing, transcreation services, project management crowdsourcing, telemarketing, etc.

Translation is datafied. We want to know everything happening in a translator’s environment so we can accurately measure how many segments are translated, or words per hour. Eye movement tracking and word suggestions have been around academia for some time but they have now crossed the barrier to commercial MT services. We even track translators’ social graphs, how the weather or news affect the translator, third party applications, how much leveraging from previous translations was used. All that information can help us to automate project management more and improve resource allocation. We are moving to a future where project management will also be automated.

An interesting parallel was drawn between industries when Jaap mentioned that food delivery people do not have a boss, they have an app. All they are interested in is where to pick up the food and where to deliver it. And that’s a kind of post-editor. Translation buyers are finding that some vendors send out their jobs out to the internet and freelancer translators do general machine translation and post-edit it. “I only had to do some minor fixes”, said one PM from a leading translation company. The fear is “how long until my client finds out he can do the same?”, that is how long until translation buyers find out they can post jobs on the internet (via an app, maybe) and pay post-editing rates to cut out project management fees? In short, will everything handled by robots in the near future? Pay-as-you-go models may change and users will become more active with the management of terminology, labelling, etc.

The representative from Athena Parthenos created some controversy by stating that creativity will help the industry survive as creativity is the realm of humans. Mark Seligman agreed as he said what machine translation cannot do is convey the emotions of humans, which is what marketing is all about. Chris Wendt, from Microsoft disagreed: “I have seen very creative neural translations”. Another possibility, according to Jaap was that post-editing will not longer be needed, there will be people behind dashboards and people doing the creative jobs.

Day 2 – Neural machine translation has cracked the language barrier in Japanese

But the juicy news came on Day 2. Presentations from Systran, Pangeanic and Google provided news about development of neural networks applied to machine translation with a particular accent on improvements in neural machine translation in Japanese, with Human Science reporting on post-editing from Google’s NMT API . Consensus run on neural machine translation producing more natural and fluent output than phrase-based MT. However, there are problems, too. Neural machine translation can produce unreliable output when confronted with unusual input or when a strictly literal rendering is desired. On the plus side, neural machine translation seems to be highly adaptable and it has the potential of being applied to other natural language tasks.

SDL presented UpLift, a technique similar to their old concordance check which combines words and small subsegment units which reminds me a lot of an old technique by Dejà Vu and Transit in the past. The difference now is that it is automatic, it is applied to all words in a sentence and shows translation. The back technology is the creation of a glossary “behind” the TM. This is done by creating an index (at the end of the day, when the PC is not used, according to their own recommendation). This is combined with syntactic analysis for Asian languages. The new version “repairs” fuzzy matched automatically if the difference is only a word or two (a feature also offered by our own ActivaTM). I found it striking to learn that SDL finds that people do not bother to re-use and re-train their own engines once created. Its automated training system has not been so successful (perhaps because of data privacy issues, since SDL is, at the end of the day, another LSP).

Mark Seligman gave an overview of speech to speech translation, particularly from a Japanese (and in general Asian) perspective with the first speech-to-speech product by LinguaTec (currently Lingenio) to 2017. Most of these products were ahead of their times. NEC had an Japanese-English, but the real watershed came with the app of Google Translate which gave birth to a speech translation. Jibbigo was happening in Europe at the time, too. Sony had one phrase-based app and Phraselater used by the US military. Mark provided an impressive speech-to-speech live Japanese-English translation over his app, SpeechTrans, and stated that “Google-type glasses” with subtitles or similar technology would be available in 3 years not 300.

Systran’s presentation provided a lot of information about their Open NMT initiative and how they have created a community á la Moses. I would like to write more about the value of this worthy initiative and how it may become a very significant force in a post-Moses world, although SMT systems will have life for some time.  The better outputs provided by neural machine translation in Japanese have prompted a kind of fever and much higher acceptance levels as phrase-based systems behaved with a higher degree of predictability with close language pairs. Morphologically-rich languages such as the Slavic family also proved notoriously hard to automate.

Our presentation offered information on our first results on engines built with identical datasets in French, German, Italian, Spanish, Portuguese, Russian and Japanese but using an SMT system and a neural network, with astonishing results. Systems built with identical data but in a different way (statistical versus NMT) provided rankings of “human quality” “almost human quality” in 80%-90% of the 250 sentences tested, including Russian. The improvements in neural machine translation in Japanese are real.NMT provides a better translation than the original translation

A copy of our presentation and results is available in slideshare

As Mark had previously done with a speech-to-speech system, Microsoft’s Chris Wendt provided a live test of his speech translator starting with the Star Trek sample (an alien and a human speaking to each other with a different device). The audience had to keep quiet so noise did not have an impact on the translation. Speech translation had been inspired by science fiction, yes, but it was now a reality (the same happened to Jules Verne submarines, Around the World in 80 Days, etc…) Microsoft’s neural network can accent English from non-native speakers as input. It works with Indian, French or Spanish accents, but it is not so good with strong German or Russian accents. He introduced TRUETEXT for cases where there are hesitations by actually saying what you are trying to say without hesitation, stops, etc., so that the input is more prone for machine learning.
Microsoft speech to speech translation demo Japanese text in Microsoft live demo








There are many potential uses of multilingual speech-to-speech technology: multilingual meetings, schools in the US and situations where there is one speaker and many are listening. I wonder if this may create an audience of “lazy” language learners? People asked questions to Chris in Japanese, Italian, Chinese (verbally) and Chris replied in English, which was shown in each language on the monitor. He then switched to his native German (switching the language settings in the device) and translation was provided as written text on the monitors. He still received questions in Singaporean Chinese but now the system was translating from his German into Japanese and Chinese. The system slowed down a little bit, but the leap was also great with a lot of people asking questions. Chris stated that English-Spanish is the best working combination as they are syntactically similar languages and there is also a lot of training material.

The last presentation was from Google’s Macduff Hughes, who began by addressing an audience who had already been convinced on the superiority of neural networks for Japanese English translation. “Last year NMT was a rumor, 6 months it was the beginning, and now it is here”. Hughes took Spanish as an example of one of the best language pairs and analyzed how much better and fluent neural machine translation was in comparison to phrase-based. Gender was wrong because of length in SMT in several instances, but as neural absorbs the whole sentence, it neural fixes a lot of the small annoying errors in Spanish, though not all the time.

GNMT is not ready to handle tags yet (in fact no neural system can yet). Moderate amounts of in-domain data can adapt a model. The challenge is that it can be hard to evaluate, and also automatic training, stopping and scoring. So iin this respect, there is a lot of good work that has already been done in statistical systems that cannot be imported into neural networks so easily – a conundrum faced by all MT developers.

Interestingly, Hughes pointed to experiments that prove that source sentences meaning more or less the same thing can produce similar results, which points to the fact that a kind of interlingua has been developed. Knowledge can be transferred to chat or other Neural Networks understandings.

But interlingua is another story…

web and spider crawling down

A web of problems: Why Google Translate and website translation can’t marry

It is not news that machine translated websites are penalized by search engines. Google has developed its technologies on the back of reliable bilingual website crawling and freely available public data. After ditching rule-based engines (Systran) back in 2006, it embarked on a mission to use statistical machine translation (SMT) as a byproduct of its own data analysis. Websites that use machine translation to inform users are crawled and aligned, but those alignments provide data that adds dirt (read: uncertainty) which worsens the probabilities and hence the output (read: the translation). That is why Google Translate and website translation can’t marry.

A machine translated website will be penalized by Google, for it is dirty. It is also a proof of laziness on the part of those responsible. The search giant wants to analyze natural, human data. We recently bumped into an article on that got our feathers all aflutter. In short, it proved the above point, which has been a known issue to translation companies and those offering proxy translation, often with the economical machine translation option.

web and spider crawling down

Nowadays, even e-commerce sites (see Magento help section on multilingual) do not recommend using machine translation for professional results and better ranking. It may sound ironical, but Search engines (read: Google) will penalize websites using Google Translate for their multilingual website.   Pangeanic has been a diehard advocate for quality website translation, developing Cor as a crawling and translation assistance technology that does not interfere with any of the code nor it provides machine translated output to Google’s algorithms. It checks your content, extracts the text and sends it out for translation. Whenever we hear a website or company will use raw proxy translation or simply Google Translate, we feel so sad. It is business lost, it is the cost of time wasted, wasted investment, having to face the wrong option was chosen some time ago, lose credibility… lose business and customers when the intention was to win.

Google’s violation guidelines

Google clearly bans automatically generated content (in order to avoid black hat SEO and similar techniques), including “Text translated by an automated tool without human review or curation before publishing”. Look for it in its violation guidelines. Thus, raw machine translation, unnatural results (and it is not difficult to detect a text has been produced by software) will bury your website deep in a web of penalization.   This kind of careless publication is viewed as spam or, worse still, copied or duplicated content.   You will find it hard to make up for it, unless you are prepared to probably do well what should have been done well in the first place. Follow this link to learn more about the dangers of duplicate content. 

But Pangeanic develops machine translation technologies, doesn’t it?

Yes we do. We are a well-known developer of machine translation technologies and language technologies. We use them in order to automate processes and it is particularly useful in controlled language situations, like instruction manuals and documentation for the automotive industry. It is extremely useful for gisting, to get a quick idea of what a text in a foreign language says at light speed. It also helps translators in certain situations to pre-translate and post-edit the content, which always needs a final verification in order to ensure to flows as natural language.   If your website is rather big (a large e-commerce site, for example, can contain tens of millions of words) and you decide to translate sections of your website using raw MT, there is quality option to consider. We can offer machine translation engines that are trained with your previous translations (aligned as reliable “translation memories”) which will speak your language in your style and will contain your terminology, specific to your products, services and industry. Creating engines with your own data, or customizing our own engines with your data and terminology will create better quality translations than general, online machine translation tools. Our expert translators can post-edit the content to make sure it conveys the message as it should.


This is surely one of the most difficult things to do, but it is extremely important to search engines. Your content must be informative and engaging. Bouce rates are an indication of how visitors interact with your site, but a high bounce rate may not necessarily be an indication of a bad website. Some of your pages may offer the information the visitor was looking for. The visitor leaves without interacting because he /she found the information. Check this informative post by Yoast on why a high bounce rate is not necessarily a bad thing for your website. Maybe the person spent a minute, two, three or more reading it. A machine translated website simply does not offer the quality content nor the value website visitors want. 

Multilingual SEO strategy

Keywords cannot be machine translated, people search for different things in different places.

A simple keyword like “sneakers” can serve as an example (follow this article for a list of top ten disagreements between US and British English). It is widely used in the US, although more profusely in some areas than others. British English uses “trainers” (from “training shoes”. People looking for this kind of garment will not land on your page if you are using a different keyword – and so it happens with languages. Machine translated keywords just won’t work in other languages.   Pangeanic solves this challenge by specialist translators with a flare of marketing and aware of such issues. They use our website analysis and SEO tools (SEMRush, Google AdWords, etc.) in order to check the popular options in each country/region so you can make an informed decision about how to market your products from your website, and not use a general or direct translation.

5 tips to translate a website in many languages and embed it in your business strategy

by Manuel Herranz

Large enterprises and even SME’s around the world are realizing how important it is to translate a webpage in many languages.

1. A free website translator isn’t simply enough.

It may do the job fairly well if you just need to understand a website in another language, but that kind of automatic translation is not good enough when you are looking to attract customers.

2. Free website translations published as good content send the wrong message to your potential audience.

Google can be quoted as the best example. The search giant is very aware that it is the search engine of choice used around the world and it needs to be available to everyone. Since there are still billions of people who can’t read English or understand it, Google provides the option of translating websites and search results into the language they are familiar with – but this is a quick, on-the-fly HTML conversion for information purposes only.
If you want to establish a solid business presence in many countries around the world, then you need professional website translations as well.

3. Thanks to a multilingual website containing website translations of your original product descriptions in other languages, your target audience is much wider.

You have been targeting a particular audience since the inception of your business. Translation into several languages of your web content has many benefits and one could literally write a book on them. The number one, of course is that if your website has always been monolingual, then you were only communicating with people who understood your main language. With website translation you can rank in search engines and carry your message to people who don’t understand your web’s first language. It will actually make sense to them when they visit your website, click on their language button or tab and are able to read everything that is written on translations increase SEO visibility

Brand image is the most important thing for any business in the world and brand image is not seen by looking at the size of a business or the quality of its products. Brand image is measured by looking at the people’s attitude towards a business.

A business can change people’s attitude towards it through its marketing efforts. Your brand image starts to build up as you start making some place in the hearts of your customers. That’s done through intelligent marketing, connecting advertisements and by personalizing your messages for them. When you translate a website into Spanish, you are opening up to an audience speaking the 2nd most spoken language in the world (500 million). Spanish has a strong presence not only in Europe and Latin America, but also in the United States – and brands are learning the power of marketing in US Spanish.

Pangeanic has a long relationship with Japanese companies. If you are Japanese and you decide to translate a website from Japanese to English will understand that it’s an amazing way of starting a connection with people from different corners of the world.

4. Better SEO and marketing results.

Start introducing new content on your website in multiple languages and you will see an increase in traffic and conversions – that is almost guaranteed. There are several strategies to do so, either with a multisite strategy or with a multilingual site.

Related Content – Learn more about multisite and multilingual sites for SEO:
3 Tips on translating a website and website localization

The more languages you add to a website, the more keywords search engines will detect on your site. OK, there is work to do in Analytics, regular publishing, geo-localization, website hosting speed, etc. But do not even think twice: the more languages a website contains, the higher the changes to be in the top spot in Google.

But rankings are just one objective. The point about Inbound Marketing is that your website will act as a point of reference, as a result of the knowledge it provides to its visitors. When you convert your customers into loyal customers, you can rest assured that your customers are not going anywhere else for many years to come. They will also review your services and provide testimonials. Customer loyalty is the added collateral benefit when you translate your website into different languages. You will benefit from a greater online reputation. Remember younger generations were born with the Internet. Reviews and comments, plus the corporate information you may add in their language are more relevant than marketing material in many cases.

With website translations (and not an automatic “translate website button” or a “webpage translator”) make your brand a part of people’s lives by connecting with them culturally.

5. Establish a long-term relationship with translation company.

Your website is most probably already published and content is up. And quite likely, the only place containing all the text that needs translating is… your website. It is typical for a website to develop over time. Pangeanic technologies can crawl your website and extract and text in a bilingual format for you to publish immediately, keeping a bilingual copy of all your linguistic assets.

If you publish content regularly, Our Cor technology will make it even easier for you to keep track of your publications automatically. You publish and Cor detects your new content, extracts it and sends it to a project manager or translator so it can be processed at the regular interval you require. Watch the video below to see Pangeanic’s crawler in action, keeping track of our own publications.

Lastly, if your content is high confidential and you need to translate a website but confidentiality is paramount before public release, our Client Portal makes it easy for you to upload content in a completely secure manner thanks to our encrypted solutions.


If you publish content regularly, Our Cor technology will make it even easier for you to keep track of your publications automatically. You publish and Cor detects your new content, extracts it and sends it to a project manager or translator so it can be processed at the regular interval you require