Isaac R Caswell
I do research in low-resource machine translation!
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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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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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XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
Sebastian Ruder
Mihir Sanjay Kale
Shruti Rijhwani
Jean-Michel Sarr
Cindy Wang
John Wieting
Christo Kirov
Dana L. Dickinson
Bidisha Samanta
Connie Tao
David Adelani
Vera Axelrod
Reeve Ingle
Dmitry Panteleev
Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore, pp. 1856-1884
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Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) — languages for which NLP research is particularly far behind in meeting user needs — it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks — tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text only, multi-modal (vision, audio, and text), supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models.
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Building Machine Translation Systems for the Next Thousand Languages
Ankur Bapna
Julia Kreutzer
Aditya Siddhant
Mengmeng Niu
Pallavi Nikhil Baljekar
Xavier Garcia
Vera Saldinger Axelrod
Yuan Cao
Maxim Krikun
Pidong Wang
Apu Shah
Yanping Huang
Zhifeng Chen
Yonghui Wu
Macduff Richard Hughes
Google Research (2022)
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
Julia Kreutzer
Lisa Wang
Ahsan Wahab
Nasanbayar Ulzii-Orshikh
Allahsera Auguste Tapo
Nishant Subramani
Artem Sokolov
Claytone Sikasote
Monang Setyawan
Supheakmungkol Sarin
Sokhar Samb
Benoît Sagot
Clara E. Rivera
Annette Rios
Isabel Papadimitriou
Salomey Osei
Pedro Javier Ortiz Suárez
Iroro Fred Ọ̀nọ̀mẹ̀ Orife
Kelechi Ogueji
Rubungo Andre Niyongabo
Toan Nguyen
Mathias Müller
André Müller
Shamsuddeen Hassan Muhammad
Nanda Muhammad
Ayanda Mnyakeni
Jamshidbek Mirzakhalov
Tapiwanashe Matangira
Colin Leong
Nze Lawson
Yacine Jernite
Mathias Jenny
Bonaventure F. P. Dossou
Sakhile Dlamini
Nisansa de Silva
Sakine Çabuk Ballı
Stella Biderman
Alessia Battisti
Ahmed Baruwa
Ankur Bapna
Pallavi Baljekar
Israel Abebe Azime
Ayodele Awokoya
Duygu Ataman
Orevaoghene Ahia
Oghenefego Ahia
Sweta Agrawal
Mofetoluwa Adeyemi
TACL (2022)
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With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering hundreds of languages. However, to date there has been no systematic analysis of the quality of these publicly available datasets, or whether the datasets actually contain content in the languages they claim to represent. In this work, we manually audit the quality of 205 language-specific corpora released with five major public datasets (CCAligned, ParaCrawl, WikiMatrix, OSCAR, mC4), and audit the correctness of language codes in a sixth (JW300). We find that lower-resource corpora have systematic issues: at least 15 corpora are completely erroneous, and a significant fraction contains less than 50% sentences of acceptable quality. Similarly, we find 82 corpora that are mislabeled or use nonstandard/ambiguous language codes. We demonstrate that these issues are easy to detect even for non-speakers of the languages in question, and supplement the human judgements with automatic analyses. Inspired by our analysis, we recommend techniques to evaluate and improve multilingual corpora and discuss the risks that come with low-quality data releases.
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Writing System and Speaker Metadata for 2,800+ Language Varieties
Sebastian Ruder
Clara E. Rivera
Proceedings of the Language Resources and Evaluation Conference, European Language Resources Association, Marseille, France (2022), pp. 5035-5046
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We describe an open-source dataset providing metadata for about 2,800 language varieties used in the world today. Specifically, the dataset provides the attested writing system(s) for each of these 2,800+ varieties, as well as an estimated speaker count for each variety. This data set was developed through internal research and has been used for analyses around language technologies. This is the largest publicly-available, machine-readable resource with writing system and speaker information for the world's languages. We hope the availability of this data will catalyze research in under-represented languages.
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Translationese as a Language in “Multilingual” NMT
Parker Riley
David Grangier
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online (2020), pp. 7737-7746
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Machine translation has an undesirable propensity to produce “translationese” artifacts, which can lead to higher BLEU scores while being liked less by human raters. Motivated by this, we model translationese and original (i.e. natural) text as separate languages in a multilingual model, and pose the question: can we perform zero-shot translation between original source text and original target text? There is no data with original source and original target, so we train a sentence-level classifier to distinguish translationese from original target text, and use this classifier to tag the training data for an NMT model. Using this technique we bias the model to produce more natural outputs at test time, yielding gains in human evaluation scores on both adequacy and fluency. Additionally, we demonstrate that it is possible to bias the model to produce translationese and game the BLEU score, increasing it while decreasing human-rated quality. We analyze these outputs using metrics measuring the degree of translationese, and present an analysis of the volatility of heuristic-based train-data tagging.
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Most data selection research in machine translation focuses on improving a single domain. We perform data selection for multiple domains at once. This is achieved by carefully introducing instance-level domain-relevance features and automatically constructing a training curriculum to gradually concentrate on multi-domain relevant and noise-reduced data batches. Both the choice of features and the use of curriculum are crucial for balancing and improving all domains, including out-of-domain. In large-scale experiments, the multi-domain curriculum simultaneously reaches or outperforms the individual performance and brings solid gains over no-curriculum training.
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Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context. LangID is largely treated as solved in the literature, with models reported that achieve over 90% average F1 on as many as 1,366 languages. We train LangID models on up to 1,629 languages with comparable quality on held-out test sets, but find that human-judged LangID accuracy for web-crawl text corpora created using these models is only around 5% for many lower-resource languages, suggesting a need for more robust evaluation. Further analysis revealed a variety of error modes, arising from domain mismatch, class imbalance, language similarity, and insufficiently expressive models. We propose two classes of techniques to mitigate these errors: wordlist-based tunable-precision filters (for which we release curated lists in about 500 languages) and transformer-based semi-supervised LangID models, which increase median dataset precision from 5.5% to 71.2%. These techniques enable us to create an initial data set covering 100K or more relatively clean sentences in each of 500+ languages, paving the way towards a 1,000-language web text corpus.
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BLEU might be Guilty but References are not Innocent
David Grangier
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, pp. 61-71
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The quality of automatic metrics for machine translation has been increasingly called into question, especially for high-quality systems. This paper demonstrates that, while choice of metric is important, the nature of the references is also critical. We study different methods to collect references and compare their value in automated evaluation by reporting correlation with human evaluation for a variety of systems and metrics. Motivated by the finding that typical references exhibit poor diversity, concentrating around translationese language, we develop a paraphrasing task for linguists to perform on existing reference translations, which counteracts this bias. Our method yields higher correlation with human judgment not only for the submissions of WMT 2019 English to German, but also for Back-translation and APE augmented MT output, which have been shown to have low correlation with automatic metrics using standard references. We demonstrate that our methodology improves correlation with all modern evaluation metrics we look at, including embedding-based methods.To complete this picture, we reveal that multi-reference BLEU does not improve the correlation for high quality output, and present an alternative multi-reference formulation that is more effective.
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