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Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation ...
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Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation ...
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On the Copying Behaviors of Pre-Training for Neural Machine Translation ...
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Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation ...
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On the Inference Calibration of Neural Machine Translation ...
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EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation ...
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Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models ...
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Understanding and Improving Lexical Choice in Non-Autoregressive Translation ...
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Information Aggregation for Multi-Head Attention with Routing-by-Agreement ...
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Neuron Interaction Based Representation Composition for Neural Machine Translation ...
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Multi-Granularity Self-Attention for Neural Machine Translation ...
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Towards Understanding Neural Machine Translation with Word Importance ...
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Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons ...
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Translating pro-drop languages with reconstruction models
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In: Wang, Longyue orcid:0000-0002-9062-6183 , Tu, Zhaopeng, Shi, Shuming, Zhang, Tong, Graham, Yvette and Liu, Qun orcid:0000-0002-7000-1792 (2018) Translating pro-drop languages with reconstruction models. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2–7 Feb 2018, New Orleans, LA, USA. ISBN 978-1-57735-800-8 (2018)
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Translating pro-drop languages with reconstruction models
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In: Wang, Longyue orcid:0000-0002-9062-6183 , Tu, Zhaopeng, Shi, Shuming, Zhang, Tong, Graham, Yvette and Liu, Qun orcid:0000-0002-7000-1792 (2018) Translating pro-drop languages with reconstruction models. In: 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), 2 - 7 Feb 2018, New Orleans, LA, USA. ISBN 978-1-57735-800-8 (2018)
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Translating Pro-Drop Languages with Reconstruction Models ...
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Exploiting Deep Representations for Neural Machine Translation ...
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Abstract:
Advanced neural machine translation (NMT) models generally implement encoder and decoder as multiple layers, which allows systems to model complex functions and capture complicated linguistic structures. However, only the top layers of encoder and decoder are leveraged in the subsequent process, which misses the opportunity to exploit the useful information embedded in other layers. In this work, we propose to simultaneously expose all of these signals with layer aggregation and multi-layer attention mechanisms. In addition, we introduce an auxiliary regularization term to encourage different layers to capture diverse information. Experimental results on widely-used WMT14 English-German and WMT17 Chinese-English translation data demonstrate the effectiveness and universality of the proposed approach. ... : EMNLP 2018 ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1810.10181 https://arxiv.org/abs/1810.10181
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Exploiting cross-sentence context for neural machine translation
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In: Wang, Longyue orcid:0000-0002-9062-6183 , Tu, Zhaopeng, Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2017) Exploiting cross-sentence context for neural machine translation. In: 2017 Conference on Empirical Methods in Natural Language Processing, 7-8 Sept 2017, Copenhagen, Denmark. (2017)
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