The Future of Machine Translation

Update: Machine Translation Advancements with Pangeanic's Deep Adaptive Machine Translation (as of End 2024)

The translation industry continues evolving rapidly, with machine translation (MT) systems demonstrating unparalleled advancements. By the close of 2024, Pangeanic is once more emerging as a leader in the space, driven by our cutting-edge Deep Adaptive AI Translation (DAAIT) technology. DAAIT exemplifies how AI-driven solutions can adapt dynamically to human input, offering near-human fluency across diverse contexts.

Pangeanic’s Deep Adaptive Machine Translation: Bridging AI and Human Expertise

Pangeanic's Deep Adaptive Machine Translation leverages state-of-the-art neural machine translation (NMT) algorithms that evolve with continuous human input. Unlike traditional static systems, DAAIT can integrate user-specific terminology, style, and domain-specific references during training and in real-time operation. This ensures linguistic accuracy, cultural relevance, and fluency, making it particularly valuable in scenarios requiring precision and consistency.

Human-Centric Adaptation: DAAIT incorporates human-translated references to refine outputs, resulting in translations that mimic human quality. This adaptability makes it a key tool for industries requiring nuanced and high-stakes communication.
Enhanced Fluency: By focusing on entire sentence structures and contextual understanding, DAAIT generates outputs that feel natural, meeting the growing demand for human-like MT solutions.

Real-World Applications: News Agency EFE and EV Manufacturer BYD

Pangeanic's DAMT has been successfully deployed in various high-impact use cases, underscoring its versatility and reliability:

Use Case: News Agency EFE: As one of the world's largest news agencies, EFE handles multilingual content daily. DAMT empowers its editorial teams by streamlining the translation of breaking news into multiple languages while maintaining journalistic accuracy and speed. By adapting to the agency’s editorial style, Pangeanic ensures that translations align seamlessly with EFE's established voice.
Use Case BYD (Electric Vehicle Manufacturer): Precision in documentation, marketing, and compliance materials is crucial in the highly technical and competitive EV sector. Pangeanic's DAMT supports BYD by delivering fluent, industry-specific translations that reflect the brand's commitment to innovation and sustainability. This adaptability enables BYD to communicate effectively with diverse global audiences.

Machine Translation in 2024: A Symbiosis of AI and Human Creativity

As the global MT market surpasses expectations, Pangeanic demonstrates how AI can work alongside human professionals rather than replace them. DAAIT’s success illustrates how machine translation enhances human workflows:

Speed and Volume: DAAIT enables the rapid processing of vast datasets, meeting tight deadlines without sacrificing quality.
Reducing Cognitive Load: By producing high-quality drafts, DAAIT reduces the need for extensive post-editing, allowing human translators to focus on creative and nuanced tasks.

Original Post:

It’s an exciting time for translators, with December 2017 witnessing the launch of two AI systems that can 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 translators for several specialized fields and language combinations providing tremendous growth opportunities for the industry.” This is good news for the future of machine translation and both for individual translators and translator tech developers. Both will be called upon to deliver increasingly specialized translation solutions to the growing global demand.

Technology does not Aim to Replace Translators

Before delving into the machine translation systems 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 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 the new technology is one of co-existence and 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 significant technologies influencing the global machine translation market: statistical machine translation, rule-based machine translation, hybrid machine translation, and neural machine translation. Its authors note, "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 the 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 how 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 evaluate the fluency of a sentence in the target language by analyzing a few words at a time. NMT is one of many exciting ways to elicit more natural, fluent-sounding translations in the target language. Academia still struggles to understand what happens inside the "black box" once the neural network starts to train. The "conclusions" are amazing and sometimes surprisingly accurate regarding human language processing because of the number of calculations, weights, gradients applied, and attention models, even between non-related languages and languages with 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 for documents that focus on one particular subject. Its advantages include the plethora of existing current platforms and algorithms (which make the system cheap and quick) and the fact that it requires little space (i.e., it does not need a server of its own). Training is done in CPU servers and it is easily deployed. Decoding is also fast and serves as a 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 also it works better with close languages, but it does not perform well in 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 machine translation, and it dates back to the 1950s; 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 analyze language at the semantic and syntactic levels deeply. Its weak point is the large number of rules that govern each language, which may end up contradicting each other and sometimes the "mechanic" sound.

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 excellent 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 aims not to replace human translators but rather to facilitate speed and precision.It creates a market for fast translation where humans cannot quickly translate massive amounts of data. 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. Many are arguing that post-editors' role 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.