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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning ... Siddhant, Aditya; Bapna, Ankur; Firat, Orhan. - : arXiv, 2022 BASE Show details 2 Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets Caswell, Isaac; Kreutzer, Julia; Wang, Lisa... In: https://hal.inria.fr/hal-03177623 ; 2021 (2021) BASE Show details 3 Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets ... Kreutzer, Julia; Caswell, Isaac; Wang, Lisa. - : arXiv, 2021 BASE Show details 4 Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus ... Caswell, Isaac; Breiner, Theresa; van Esch, Daan; Bapna, Ankur. - : arXiv, 2020 Abstract: 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 ... : Accepted to COLING 2020. 9 pages with 8 page abstract ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://arxiv.org/abs/2010.14571 https://dx.doi.org/10.48550/arxiv.2010.14571 BASE Hide details 5 BLEU might be Guilty but References are not Innocent ... Freitag, Markus; Grangier, David; Caswell, Isaac. - : arXiv, 2020 BASE Show details 6 Investigating Multilingual NMT Representations at Scale ... Kudugunta, Sneha Reddy; Bapna, Ankur; Caswell, Isaac. - : arXiv, 2019 BASE Show details 7 Translationese as a Language in "Multilingual" NMT ... Riley, Parker; Caswell, Isaac; Freitag, Markus. - : arXiv, 2019 BASE Show details

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