Google Translate Utilizes Zero-Resource Machine Translation To Support New Languages

This Article Is Based On The Research Paper 'BUILDING MACHINE TRANSLATION SYSTEMS FOR THE NEXT THOUSAND LANGUAGES'. All Credit For This Research Goes To The Researchers šŸ‘šŸ‘šŸ‘

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Machine translation (MT) technology has advanced significantly. The quality of translation services has increased, and they have grown to incorporate new languages, according to research benchmarks such as WMT. Even though existing translation services cover languages spoken by most people throughout the world, they only cover about 100 languages, accounting for slightly over 1% of all languages spoken globally. Furthermore, the covered languages are predominantly European, generally ignoring linguistically diverse regions such as Africa and the Americas.

Building functional translation models for the long tail of languages face two major roadblocks. The first is data scarcity; digitized material for many languages is scarce, and finding it on the web can be difficult due to quality concerns with Language Identification (LangID) models. With monolingual data collected from the web, the next issue is to develop high-quality, general-domain MT models from small amounts of monolingual training data. They use a pragmatic approach, leveraging all parallel data available for higher resource languages to improve the quality of long-tail languages with only monolingual. They call this setting zero-resource because no direct supervision is known for our long-tail languages. The second issue emerges as a result of modeling restrictions. Without such data, models must learn to translate from small amounts of monolingual text, a new area of research. These issues must be addressed for translation models to achieve adequate quality. In “Building Machine Translation Systems for the Next Thousand Languages,” they describe how to create high-quality monolingual datasets for over a thousand languages that lack translation and train MT models using monolingual data alone. They expanded Google Translate to add 24 under-resourced languages as part of this work. They developed and utilized specific neural language identification models and unique filtering procedures to build monolingual datasets for these languages. The strategies they provide supplement massively multilingual models with a self-supervised job to enable zero-resource translation. Finally, let’s discuss how native speakers assisted us in achieving this goal.

Meet the Data

It’s far more complex than it appears to automatically collect useful textual data for under-resourced languages. Many publicly available datasets crawled from the web typically include more noise than usable data for the languages they seek to cover. Tasks like LangID, which work well for high-resource languages, fail for low-resource languages. They discovered that the dataset was too noisy to be helpful in our early attempts to identify under-resourced languages on the web by training a typical Compact Language Detector v3 (CLD3) LangID model.

They trained a Transformer-based, semi-supervised LangID model on over 1000 languages as an alternative. This model combines the LangID job with the MAsked Sequence-to-Sequence (MASS) task to generalize across noisy online data. MASS essentially mutilates the input by deleting token sequences at random and then training the model to predict these sequences. They used a dataset filtered with a CLD3 model and trained to recognize clusters of comparable languages to apply the Transformer-based model. Then used, the open-source Term Frequency-Inverse Internet Frequency (TF-IIF) filtering to locate and delete sentences in related high-resource languages and construct a range of language-specific filters to exclude certain diseases. The end result was a dataset of monolingual text in over 1000 languages, with over 100,000 phrases in 400 of them. They conducted human evaluations on 68 of these languages and discovered that the vast majority (>70%) reflected high-quality, in-language information.


1000 languages web-crawled datasets: The absence of digitized and accessible information and NLP tools like language identification (LangID) models has hampered progress in developing machine translation systems in these languages. Such resources are abundant for higher education.

Meet the Models

They devised a simple yet practical approach for zero-resource translation, i.e., translation for languages with no in-language parallel text and no language-specific translation examples, after we obtained a collection of monolingual text in over 1000 languages.

They integrate all accessible parallel text data with millions of instances for higher resource languages to enable the model to learn the translation task rather than limiting it to an artificial scenario with only monolingual text. Using the MASS challenge, they simultaneously train the model to learn representations of under-resourced languages directly from the monolingual text. To complete this objective, the model must construct a comprehensive presentation of the language in issue and a detailed knowledge of how words interact with each other in a sentence.

Any input the model sees during training has a particular token specifying which language the output should be in, much like the typical multilingual translation formulation. The use of the exact unique tickets for both the monolingual MASS job and the translation task is an additional innovation. As a result, the token to french may signal that the source is in English and has to be translated to French (the translation assignment) or that the reference is in garbled French and requires fluent translation (the MASS task). A translation to a french tag takes on the meaning “Produce a fluent output in French that is semantically near to the input,” regardless of whether the input is garbled in the same language or in a different language entirely, by employing the same tags for both jobs. There isn’t much of a distinction between the two from the model’s standpoint.

Surprisingly, this straightforward approach yields excellent zero-shot translations. The resulting model’s BLEU and ChrF scores are in the 10ā€“40 and 20ā€“60 ranges, indicating mid- to high-quality translation.These metrics, however, were only computed on a tiny portion of languages with human-translated evaluation sets. They developed evaluation criteria based on round-trip translation to better evaluate the quality of translation for the remaining languages. We were able to see that several hundred languages are nearing high translation quality.

They use the model to generate vast amounts of synthetic parallel data, filter the data using round-trip translation, and train the model on this filtered synthetic data using back-translation and self-training. Finally, we refine the model on a subset of 30 languages before distilling it into a model that can be served



Native Speakers’ Contributions

Research required regular communication with native speakers of these languages. They worked with approximately 100 people from Google and other organizations who were fluent in these languages. Some volunteers assisted in developing specific filters to remove text that was not recognized by automatic methods, such as Hindi combined with Sanskrit. Others helped with transliteration between different scripts used by the languages, such as Meetei Mayek and Bengali, for which adequate tools were not available. In contrast, still, others assisted with a variety of evaluative chores.

Final Thoughts

Native speakers were also crucial in advising on aspects of political sensitivity, such as the proper name for the language and the writing system to adopt.

This is a significant first step in supporting more language technologies in languages with limited resources. Most significantly, we want to emphasize that the quality of translations produced by these models is still far inferior to that of Google Translate’s higher-resource languages. These models are a good starting point for interpreting information in under-resourced languages, but they will make mistakes and have biases. The output should be carefully considered, as with any ML-driven tool.



I am consulting intern at MarktechPost. I am majoring in Mechanical Engineering at IIT Kanpur. My interest lies in the field of machining and Robotics. Besides, I have a keen interest in AI, ML, DL, and related areas. I am a tech enthusiast and passionate about new technologies and their real-life uses.