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1
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation ...
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Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models ...
Abstract: Machine translation (MT) systems translate text between different languages by automatically learning in-depth knowledge of bilingual lexicons, grammar and semantics from the training examples. Although neural machine translation (NMT) has led the field of MT, we have a poor understanding on how and why it works. In this paper, we bridge the gap by assessing the bilingual knowledge learned by NMT models with phrase table -- an interpretable table of bilingual lexicons. We extract the phrase table from the training examples that an NMT model correctly predicts. Extensive experiments on widely-used datasets show that the phrase table is reasonable and consistent against language pairs and random seeds. Equipped with the interpretable phrase table, we find that NMT models learn patterns from simple to complex and distill essential bilingual knowledge from the training examples. We also revisit some advances that potentially affect the learning of bilingual knowledge (e.g., back-translation), and report some ... : 10 pages ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://arxiv.org/abs/2004.13270
https://dx.doi.org/10.48550/arxiv.2004.13270
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3
Towards Understanding Neural Machine Translation with Word Importance ...
He, Shilin; Tu, Zhaopeng; Wang, Xing. - : arXiv, 2019
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