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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 Hits 1 – 20 of 69 1 Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale ... Sartran, Laurent; Barrett, Samuel; Kuncoro, Adhiguna. - : arXiv, 2022 BASE Show details 2 Diverse Pretrained Context Encodings Improve Document Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Donato, Domenic; Dyer, Chris. - : Underline Science Inc., 2021 BASE Show details 3 Better {C}hinese Sentence Segmentation with Reinforcement Learning ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Dyer, Chris; Srinivasan, Srivatsan. - : Underline Science Inc., 2021 BASE Show details 4 Learning Robust and Multilingual Speech Representations ... Kawakami, Kazuya; Wang, Luyu; Dyer, Chris. - : arXiv, 2020 BASE Show details 5 Better Document-Level Machine Translation with Bayes’ Rule Yu, Lei; Sartran, Laurent; Stokowiec, Wojciech; Ling, Wang; Kong, Lingpeng; Blunsom, Phil; Dyer, Chris In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 346-360 (2020) (2020) Abstract: We show that Bayes’ rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents a compelling benefit because parallel documents are not always available. In our formulation, the posterior probability of a candidate translation is the product of the unconditional (prior) probability of the candidate output document and the “reverse translation probability” of translating the candidate output back into the source language. Our proposed model uses a powerful autoregressive language model as the prior on target language documents, but it assumes that each sentence is translated independently from the target to the source language. Crucially, at test time, when a source document is observed, the document language model prior induces dependencies between the translations of the source sentences in the posterior. The model’s independence assumption not only enables efficient use of available data, but it additionally admits a practical left-to-right beam-search algorithm for carrying out inference. Experiments show that our model benefits from using cross-sentence context in the language model, and it outperforms existing document translation approaches. Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doi.org/10.1162/tacl_a_00319 https://doaj.org/article/bc1081a128a245a79fae343c5a133969 BASE Hide details 6 Learning and Evaluating General Linguistic Intelligence ... Yogatama, Dani; d'Autume, Cyprien de Masson; Connor, Jerome. - : arXiv, 2019 BASE Show details 7 Shallow Syntax in Deep Water ... Swayamdipta, Swabha; Peters, Matthew; Roof, Brendan. - : arXiv, 2019 BASE Show details 8 Unsupervised Bilingual POS Tagging with Markov Random Fields ... Desai Chen; Dyer, Chris; Cohen, Shay B.. - : Figshare, 2018 BASE Show details 9 Unsupervised Bilingual POS Tagging with Markov Random Fields ... Desai Chen; Dyer, Chris; Cohen, Shay B.. - : Figshare, 2018 BASE Show details 10 Knowledge-Rich Morphological Priors for Bayesian Language Models ... Chahuneau, Victor; Smith, Noah A.; Dyer, Chris. - : Figshare, 2018 BASE Show details 11 Learning to Discover, Ground and Use Words with Segmental Neural Language Models ... Kawakami, Kazuya; Dyer, Chris; Blunsom, Phil. - : arXiv, 2018 BASE Show details 12 Paraphrase-Supervised Models of Compositionality ... Saluja, Avneesh; Dyer, Chris; Ruvini, Jean-David. - : arXiv, 2018 BASE Show details 13 From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP (Dagstuhl Seminar 17042) Cho, Kyunghyun; Dyer, Chris; Blunsom, Phil. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2017. : Dagstuhl Reports. Dagstuhl Reports, Volume 7, Issue 1, 2017 BASE Show details 14 Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling ... Kawakami, Kazuya; Dyer, Chris; Blunsom, Phil. - : arXiv, 2017 BASE Show details 15 From Characters to Understanding Natural Language (C2NLU): Robust End-to-End Deep Learning for NLP (Dagstuhl Seminar 17042) ... Blunsom, Phil; Cho, Kyunghyun; Dyer, Chris. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany, 2017 BASE Show details 16 Ontology-Aware Token Embeddings for Prepositional Phrase Attachment ... Dasigi, Pradeep; Ammar, Waleed; Dyer, Chris. - : arXiv, 2017 BASE Show details 17 Massively Multilingual Word Embeddings ... Ammar, Waleed; Mulcaire, George; Tsvetkov, Yulia. - : arXiv, 2016 BASE Show details 18 Many Languages, One Parser ... Ammar, Waleed; Mulcaire, George; Ballesteros, Miguel. - : arXiv, 2016 BASE Show details 19 Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning ... Tsvetkov, Yulia; Manaal Faruqui; Ling, Wang. - : Carnegie Mellon University, 2016 BASE Show details 20 Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning ... Tsvetkov, Yulia; Manaal Faruqui; Ling, Wang. - : Carnegie Mellon University, 2016 BASE Show details Page: 1 2 3 4

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