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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; Siddhant, Aditya; Firat, Orhan; Fu, Jinlan; Liu, Pengfei; Hu, Junjie; Garrette, Dan; Neubig, Graham; Johnson, Melvin. - : arXiv, 2021 Abstract: Machine learning has brought striking advances in multilingual natural language processing capabilities over the past year. For example, the latest techniques have improved the state-of-the-art performance on the XTREME multilingual benchmark by more than 13 points. While a sizeable gap to human-level performance remains, improvements have been easier to achieve in some tasks than in others. This paper analyzes the current state of cross-lingual transfer learning and summarizes some lessons learned. In order to catalyze meaningful progress, we extend XTREME to XTREME-R, which consists of an improved set of ten natural language understanding tasks, including challenging language-agnostic retrieval tasks, and covers 50 typologically diverse languages. In addition, we provide a massively multilingual diagnostic suite (MultiCheckList) and fine-grained multi-dataset evaluation capabilities through an interactive public leaderboard to gain a better understanding of such models. The leaderboard and code for ... : EMNLP 2021 camera-ready ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2104.07412 https://dx.doi.org/10.48550/arxiv.2104.07412 BASE Hide details 17 When Does Translation Require Context? A Data-driven, Multilingual Exploration ... Yin, Kayo; Fernandes, Patrick; Martins, André F. T.. - : arXiv, 2021 BASE Show 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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