The Best Machine Translation Software and Services in 2024

Machine translation has come a long way in recent years, from its humble beginnings in the 1950s and the initial rule-based systems to statistical machine translation in the first decade of the 2000s, to advance to neural machine translation rapidly, and now LLM translation. Machine Translation is a true enabler, integrated into most tools, such as our ECOChat virtual assistant (try it on our homepage). MT offers powerful tools for businesses and individuals alike as in our increasingly connected world, breaking down language barriers has never been more critical: companies are not just international or multinational, they are borderless. This article examines some of the leading machine translation software companies and services currently available, referencing our recent mention by Gartner in their Hype Cycle for Language Technologies for 2023 and 2024.

LLM Translation, Multilingual Model, NMT and Automatic Post-Editing (APE)

 

Multilingual Model Machine Translation

1. Google Translate

Google Translate remains one of the most widely used machine translation services. It supports over 200 languages and boasts a powerful and renowned translation panel, a pioneer in online MT services. Google Translate handles many minority or low-resourced. It offers:

• Website translation
• Mobile apps for on-the-go translation
• Real-time conversation mode
• Image translation

While not always perfect, Google Translate's vast language coverage and ease of use make it a go-to choice for many users. However, Google Translate does not offer any custom engines or the ability to automatically post-edit content.

1. DeepL

DeepL has gained a strong reputation for producing translations that sound very natural, particularly for European languages, although some users find the translations "too free" at times. Key features include:

• Support for 29 languages (as of 2024)
• A user-friendly interface
• Integration with Microsoft Office
• API access for developers

Many users find DeepL's translations to be quite context-aware and fluent compared to other services. However, DeepL does not offer custom engines or the ability to automatically post-edit content, although users can manually correct translations on the Write panel.

3. Microsoft Bing Translator

Microsoft's offering integrates well with its suite of productivity tools. Notable features:

• Support for over 60 languages
• Real-time conversation translation
• Custom translation models for specific industries
• API integration for developers

Microsoft Translator is particularly useful for businesses already using Microsoft's ecosystem. However, Microsoft Bing Translator does not offer the possibility to create custom engines nor the ability to automatically post-edit content.

4. Amazon Translate

Amazon Web Services (AWS) offers Amazon Translate, a powerful tool for businesses seeking to integrate machine translation into their applications. Key points:

• Pay-as-you-go pricing model
• Customizable translation models
• Integration with other AWS services
• Support for 75+ languages

Amazon Translate is an excellent choice for developers and businesses requiring scalable translation solutions.

5. SYSTRAN

One of the oldest players in the machine translation field, SYSTRAN offers:

• Both on-premises and cloud-based solutions
• Industry-specific translation models
• Support for over 55 languages
• Strong data security features

SYSTRAN is particularly popular among government agencies and large corporations requiring high levels of data protection.

Rule-based MT is not dead. In fact, before the advent of LLMs, it made a lot of sense for language pairs for which rules could be built. One of the best examples is Apertium. In an era dominated by neural machine translation and large language models, it's tempting to dismiss rule-based machine translation (RBMT) as an obsolete technology. However, this perspective overlooks the continued relevance and effectiveness of rule-based systems, particularly for specific language pairs and use cases. Rule-based machine translation (MT) is not dead—in fact, before the advent of large language models (LLMs), it made compelling sense for language pairs where comprehensive linguistic rules could be systematically built and maintained.

What is Apertium?

Apertium is a free, open-source machine translation platform designed around the principle of shallow-transfer machine translation. The system is particularly effective for translating between closely related languages, such as those within the same language family. Its architecture consists of several key components:

Morphological Analysis: The system first analyzes the source text to identify word forms, parts of speech, and morphological features like tense, number, and gender.

Part-of-Speech Disambiguation: Using statistical models trained on annotated corpora, Apertium resolves ambiguities in grammatical categories.

Shallow Transfer: Rather than building complete syntactic trees, Apertium applies transfer rules that handle local syntactic rearrangements and lexical substitutions.

Morphological Generation: Finally, the system generates the appropriate word forms in the target language based on the transferred lexical units and their features.

Language Pairs and Success Stories

Apertium has developed translation engines for over 40 language pairs, with particularly strong performance for:

• Romance Languages: Spanish-Catalan, French-Spanish, Portuguese-Galician
• Scandinavian Languages: Norwegian-Danish, Swedish-Danish
• Turkic Languages: Various pairs within the Turkic language family
• Regional Languages: Many minority and regional language pairs that lack sufficient data for neural approaches

The Spanish-Catalan pair exemplifies Apertium's strengths. These closely related languages share significant morphological and syntactic similarities, enabling rule-based transfer to achieve high-quality translations that often rival or surpass those of neural systems, particularly for formal texts and technical documentation.

Apertium's Open Source Advantage

Apertium's open-source nature has been crucial to its success and longevity. The platform has fostered a global community of linguists, developers, and language enthusiasts who contribute rules, linguistic data, and improvements. This collaborative approach has several benefits:

• Community-Driven Development: Native speakers and linguistic experts can directly contribute to improving their language pairs

• Rapid Deployment: New language pairs can be developed relatively quickly when there's community interest

• Educational Value: The transparent rule-based approach makes Apertium an excellent tool for teaching computational linguistics

  Sustainability: The open-source model ensures the platform's continued development regardless of commercial pressures

Contemporary Relevance

While neural machine translation has achieved remarkable results for high-resource language pairs, rule-based systems like Apertium continue to serve essential niches:

• Under-resourced Languages: Many language pairs lack the millions of parallel sentences needed to train effective neural models. Rule-based approaches can achieve reasonable quality with minimal data.
• Consistent Terminology: In technical and legal domains, consistency is often more important than naturalness. Rule-based systems excel at maintaining terminological consistency across documents.
• Computational Efficiency: Apertium can run on modest hardware and doesn't require GPU acceleration, making it accessible for deployment in resource-constrained environments.
• Hybrid Approaches: Modern MT systems increasingly combine multiple approaches. Rule-based components can handle morphologically rich languages' complexity while neural components manage semantic nuances.

Key Takeaways

The field of machine translation is rapidly evolving, with improvements in neural network technology and large language models (LLMs) pushing the boundaries of what is possible. While no solution is perfect, these top providers offer powerful tools to bridge language gaps in our global society.

When selecting a machine translation service, consider factors such as language coverage, terminology management, confidence scores, accuracy, pricing, integration capabilities within your existing workflows, and specific features that align with your particular needs. As technology continues to advance, we can expect even more impressive developments in the world of machine translation.