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Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation ...
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Better Neural Machine Translation by Extracting Linguistic Information from BERT ...
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Pointer-based Fusion of Bilingual Lexicons into Neural Machine Translation ...
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Abstract:
Neural machine translation (NMT) systems require large amounts of high quality in-domain parallel corpora for training. State-of-the-art NMT systems still face challenges related to out-of-vocabulary words and dealing with low-resource language pairs. In this paper, we propose and compare several models for fusion of bilingual lexicons with an end-to-end trained sequence-to-sequence model for machine translation. The result is a fusion model with two information sources for the decoder: a neural conditional language model and a bilingual lexicon. This fusion model learns how to combine both sources of information in order to produce higher quality translation output. Our experiments show that our proposed models work well in relatively low-resource scenarios, and also effectively reduce the parameter size and training cost for NMT without sacrificing performance. ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1909.07907 https://arxiv.org/abs/1909.07907
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Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing ...
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An Easily Extensible HMM Word Aligner
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In: Prague Bulletin of Mathematical Linguistics , Vol 111, Iss 1, Pp 87-96 (2018) (2018)
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A Python-based Interface for Wide Coverage Lexicalized Tree-adjoining Grammars
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In: Prague Bulletin of Mathematical Linguistics , Vol 103, Iss 1, Pp 139-159 (2015) (2015)
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Analysis of Semi-Supervised Learning with the Yarowsky Algorithm ...
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Combining labeled and unlabeled data in statistical natural language parsing
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In: Dissertations available from ProQuest (2002)
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Separating Dependency from Constituency in a Tree Rewriting System ...
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The Conflict Between Future Tense and Modality: The Case of Will in English
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In: University of Pennsylvania Working Papers in Linguistics (1998)
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