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1
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale ...
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2
Diverse Pretrained Context Encodings Improve Document Translation ...
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3
Better {C}hinese Sentence Segmentation with Reinforcement Learning ...
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4
Learning Robust and Multilingual Speech Representations ...
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5
Better Document-Level Machine Translation with Bayes’ Rule
In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 346-360 (2020) (2020)
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6
Learning and Evaluating General Linguistic Intelligence ...
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7
Shallow Syntax in Deep Water ...
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8
Unsupervised Bilingual POS Tagging with Markov Random Fields ...
Desai Chen; Dyer, Chris; Cohen, Shay B.. - : Figshare, 2018
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9
Unsupervised Bilingual POS Tagging with Markov Random Fields ...
Desai Chen; Dyer, Chris; Cohen, Shay B.. - : Figshare, 2018
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10
Knowledge-Rich Morphological Priors for Bayesian Language Models ...
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11
Learning to Discover, Ground and Use Words with Segmental Neural Language Models ...
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12
Paraphrase-Supervised Models of Compositionality ...
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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
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14
Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling ...
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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
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16
Ontology-Aware Token Embeddings for Prepositional Phrase Attachment ...
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17
Massively Multilingual Word Embeddings ...
Abstract: We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use dictionaries and monolingual data; they do not require parallel data. Our new evaluation method, multiQVEC-CCA, is shown to correlate better than previous ones with two downstream tasks (text categorization and parsing). We also describe a web portal for evaluation that will facilitate further research in this area, along with open-source releases of all our methods. ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.1602.01925
https://arxiv.org/abs/1602.01925
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18
Many Languages, One Parser ...
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19
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning ...
Tsvetkov, Yulia; Manaal Faruqui; Ling, Wang. - : Carnegie Mellon University, 2016
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20
Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning ...
Tsvetkov, Yulia; Manaal Faruqui; Ling, Wang. - : Carnegie Mellon University, 2016
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