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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; Niehues, Jan In: Transactions of the Association for Computational Linguistics, 7, 313–325 ; ISSN: 2307-387X (2022) Abstract: Speech translation has traditionally been approached through cascaded models consisting of a speech recognizer trained on a corpus of transcribed speech, and a machine translation system trained on parallel texts. Several recent works have shown the feasibility of collapsing the cascade into a single, direct model that can be trained in an end-to-end fashion on a corpus of translated speech. However, experiments are inconclusive on whether the cascade or the direct model is stronger, and have only been conducted under the unrealistic assumption that both are trained on equal amounts of data, ignoring other available speech recognition and machine translation corpora. In this paper, we demonstrate that direct speech translation models require more data to perform well than cascaded models, and although they allow including auxiliary data through multi-task training, they are poor at exploiting such data, putting them at a severe disadvantage. As a remedy, we propose the use of end-to-end trainable models with two attention mechanisms, the first establishing source speech to source text alignments, the second modeling source to target text alignment. We show that such models naturally decompose into multi-task–trainable recognition and translation tasks and propose an attention-passing technique that alleviates error propagation issues in a previous formulation of a model with two attention stages. Our proposed model outper-forms all examined baselines and is able to exploit auxiliary training data much more effectively than direct attentional models. Keyword: DATA processing & computer science; ddc:004; info:eu-repo/classification/ddc/004 URL: https://publikationen.bibliothek.kit.edu/1000145064 https://publikationen.bibliothek.kit.edu/1000145064/148663710 https://doi.org/10.5445/IR/1000145064 BASE Hide 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.. - : 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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