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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 Page: 1 2 3 4 5 6 Hits 1 – 20 of 116 1 Multilingual CoNaLa Datset, train data ... Zhiruo Wang; Cuenca, Grace; Shuyan Zhou. - : Zenodo, 2022 BASE Show details 2 Multilingual CoNaLa Datset, train data ... Zhiruo Wang; Cuenca, Grace; Shuyan Zhou. - : Zenodo, 2022 BASE Show details 3 AUTOLEX: An Automatic Framework for Linguistic Exploration ... Chaudhary, Aditi; Sheikh, Zaid; Mortensen, David R. - : arXiv, 2022 BASE Show details 4 MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages ... Wang, Zhiruo; Cuenca, Grace; Zhou, Shuyan. - : arXiv, 2022 BASE Show details 5 A Systematic Evaluation of Large Language Models of Code ... Xu, Frank F.; Alon, Uri; Neubig, Graham. - : arXiv, 2022 BASE Show details 6 Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation ... Wang, Xinyi; Ruder, Sebastian; Neubig, Graham. - : arXiv, 2022 BASE Show details 7 Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation Neubig, Graham; Sperber, Matthias; Waibel, Alex... In: Transactions of the Association for Computational Linguistics, 7, 313–325 ; ISSN: 2307-387X (2022) BASE Show details 8 Lightly Supervised Quality Estimation Waibel, Alex; Niehues, Jan; Stüker, Sebastian. - : Association for Computational Linguistics, 2022 BASE Show details 9 MasakhaNER: Named entity recognition for African languages Adelani, David,; Abbott, Jade; Neubig, Graham... In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021) BASE Show details 10 Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties ... Siminyu, Kathleen; Li, Xinjian; Anastasopoulos, Antonios. - : arXiv, 2021 BASE Show details 11 Few-shot Language Coordination by Modeling Theory of Mind ... Zhu, Hao; Neubig, Graham; Bisk, Yonatan. - : arXiv, 2021 BASE Show details 12 Systematic Inequalities in Language Technology Performance across the World's Languages ... Blasi, Damián; Anastasopoulos, Antonios; Neubig, Graham. - : arXiv, 2021 BASE Show details 13 Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models ... Huang, Po-Yao; Patrick, Mandela; Hu, Junjie. - : arXiv, 2021 BASE Show details 14 Multi-view Subword Regularization ... Wang, Xinyi; Ruder, Sebastian; Neubig, Graham. - : arXiv, 2021 BASE Show details 15 MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning ... Xia, Mengzhou; Zheng, Guoqing; Mukherjee, Subhabrata. - : arXiv, 2021 BASE Show details 16 XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ... Ruder, Sebastian; Constant, Noah; Botha, Jan. - : arXiv, 2021 BASE Show details 17 When Does Translation Require Context? A Data-driven, Multilingual Exploration ... Yin, Kayo; Fernandes, Patrick; Martins, André F. T.; Neubig, Graham. - : arXiv, 2021 Abstract: Although proper handling of discourse phenomena significantly contributes to the quality of machine translation (MT), common translation quality metrics do not adequately capture them. Recent works in context-aware MT attempt to target a small set of these phenomena during evaluation. In this paper, we propose a new metric, P-CXMI, which allows us to identify translations that require context systematically and confirm the difficulty of previously studied phenomena as well as uncover new ones that have not been addressed in previous work. We then develop the Multilingual Discourse-Aware (MuDA) benchmark, a series of taggers for these phenomena in 14 different language pairs, which we use to evaluate context-aware MT. We find that state-of-the-art context-aware MT models find marginal improvements over context-agnostic models on our benchmark, which suggests current models do not handle these ambiguities effectively. We release code and data to invite the MT research community to increase efforts on ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2109.07446 https://arxiv.org/abs/2109.07446 BASE Hide details 18 Breaking Down Multilingual Machine Translation ... Chiang, Ting-Rui; Chen, Yi-Pei; Yeh, Yi-Ting. - : arXiv, 2021 BASE Show details 19 Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ... Wang, Xinyi; Tsvetkov, Yulia; Ruder, Sebastian. - : arXiv, 2021 BASE Show details 20 Distributionally Robust Multilingual Machine Translation ... Zhou, Chunting; Levy, Daniel; Li, Xian. - : arXiv, 2021 BASE Show details Page: 1 2 3 4 5 6

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