Machine Translation
Machine Translation is an excellent example of how cutting-edge research and world-class infrastructure come together at Google. We focus our research efforts on developing statistical translation techniques that improve with more data and generalize well to new languages. Our large scale computing infrastructure allows us to rapidly experiment with new models trained on web-scale data to significantly improve translation quality. This research backs the translations served at translate.google.com, allowing our users to translate text, web pages and even speech. Deployed within a wide range of Google services like GMail, Books, Android and web search, Google Translate is a high-impact, research-driven product that bridges language barriers and makes it possible to explore the multilingual web in 90 languages. Exciting research challenges abound as we pursue human quality translation and develop machine translation systems for new languages.
Recent Publications
Connecting Language Technologies with Rich, Diverse Data Sources Covering Thousands of Languages
Sandy Ritchie
Sebastian Ruder
Julia Kreutzer
Clara Rivera
Ishank Saxena
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
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Contrary to common belief, there are rich and diverse data sources available for many thousands of languages, which can be used to develop technologies for these languages. In this paper, we provide an overview of some of the major online data sources, the types of data that they provide access to, potential applications of this data, and the number of languages that they cover. Even this covers only a small fraction of the data that exists; for example, printed books are published in many languages but few online aggregators exist.
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We present Mu2SLAM, a multilingual sequence-to-sequence model pre-trained jointly on un-labeled speech, unlabeled text and supervised data spanning Automatic Speech Recognition(ASR), Automatic Speech Translation (AST)and Machine Translation (MT), in over 100 languages. By leveraging a quantized representation of speech as a target, Mu2SLAM trains ona sequence-to-sequence masked denoising objective similar to T5 on both unlabeled speech and text, while utilizing the supervised tasks to improve cross-lingual and cross-modal representation alignment within the model. On CoVoSTAST, Mu2SLAM establishes a new state-of-the-art for models trained on public datasets, improv-ing on xx-en translation over the previous best by 1.9 Bleu points and on en-xx translation by 0.9 Bleu points. On Voxpopuli ASR, our model matches the performance of a mSLAM model finetuned with a RNN-T decoder, despite using a relatively weaker sequence-to-sequence architecture. On text understanding tasks, our model improves by more than 6% over mSLAM on XNLI, getting closer to the performance of mT5 models of comparable capacity on XNLI and TydiQA, paving the way towards a single model for all speech and text understanding tasks.
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Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translation
Behrooz Ghorbani
Patrick Fernandes
Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore, pp. 9198-9209
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Recent advances in machine translation (MT) have shown that Minimum Bayes Risk (MBR) decoding can be a powerful alternative to beam search decoding, especially when combined with neural-based utility functions. However, the performance of MBR decoding depends heavily on how and how many candidates are sampled from the model. In this paper, we explore how different sampling approaches for generating candidate lists for MBR decoding affect performance. We evaluate popular sampling approaches, such as ancestral, nucleus, and top-k sampling. Based on our insights into their limitations, we experiment with the recently proposed epsilon-sampling approach, which prunes away all tokens with a probability smaller than epsilon, ensuring that each token in a sample receives a fair probability mass. Through extensive human evaluations, we demonstrate that MBR decoding based on epsilon-sampling significantly outperforms not only beam search decoding, but also MBR decoding with all other tested sampling methods across four language pairs.
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WMT23 Metrics shared task Submission: Quality Estimation using Minimum Bayes Risk
Subhajit Naskar
Proceedings of the Eighth Conference on Machine Translation, Association for Computational Linguistics, Singapore (2023), pp. 806-811
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This report describes the Minimum Bayes Risk Quality Estimation (MBR-QE) submission to the Workshop on Machine Translation's 2023 Metrics Shared Task. MBR decoding with neural utility metrics (BLEURT) are known to be very effective in generating high quality machine translations. We use the underlying assumption of MBR decoding and develop a MBR based reference-free quality estimation metric. Our method uses a evaluator machine translation system and a reference-based utility metric (BLEURT, MeticX) to calculate a quality estimation score of a model. We report results related to comparing different MBR configuration and utility metrics.
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Neural machine translation (NMT) has progressed rapidly over the past several years, and modern models are able to achieve relatively high quality using only monolingual text data, an approach dubbed Unsupervised Machine Translation, or UNMT. However, these models still struggle in a variety of ways, including aspects of translation that for a human are the easiest---for instance, correctly translating common nouns. This work explores a cheap and abundant resource to combat this problem: bilingual lexicons (\textsc{BiLex}s). We test the efficacy of bilingual lexicons in a real-world set-up, on 200-language translation models trained on web-mined text. We present several findings: (1) we demonstrate the most effective ways to use this resource for MT by extensively experimenting with lexical data augmentation techniques, such as codeswitching and lexical prompting; (2) we pinpoint what settings and languages are benefited most from lexical data augmentation; and (3) we provide an empirical, per-language analysis of the quality of the public resource PanLex, a multilingual lexicon covering thousands of languages.
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The Devil is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation
Patrick Fernandes
Mara Finkelstein
André Martins
Graham Neubig
Ankush Garg
Conference on Machine Translation (2023)
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Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems. While considerable progress has been made on direct estimation of quality scores, the resulting metrics lack the informativeness of more detailed schemes that annotate individual errors, such as Multidimensional Quality Metrics (MQM). In this paper, we fill this gap by proposing \textbf{\textsc{AutoMQM}}, a prompting technique which leverages the \textit{reasoning} and \textit{in-context learning} capabilities of large language models (LLMs) and asks them to identify and categorize errors in translations. We start by evaluating recent LLMs, such as PaLM and PaLM-2, through simple \textit{score prediction} prompting, and we study the impact of labeled data through in-context learning and finetuning. We then evaluate \textsc{AutoMQM} with PaLM-2 models, and we find that it improves performance compared to just prompting for scores (with particularly large gains for larger models) while providing interpretability through error spans that align with human annotations.
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