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Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation ...
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TexSmart: A System for Enhanced Natural Language Understanding ...
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Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction ...
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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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deepQuest-py: large and distilled models for quality estimation
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deepQuest-py: large and distilled models for quality estimation
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In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations ; 382 ; 389 (2021)
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On the Inference Calibration of Neural Machine Translation ...
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Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models ...
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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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Abstract:
Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information. However, prior work on statistical machine translation has shown that extending the basic translation unit from words to phrases has produced substantial improvements, suggesting the possibility of improving NMT performance from explicit modeling of phrases. In this work, we present multi-granularity self-attention (Mg-Sa): a neural network that combines multi-head self-attention and phrase modeling. Specifically, we train several attention heads to attend to phrases in either n-gram or syntactic formalism. Moreover, we exploit interactions among phrases to enhance the strength of structure modeling - a commonly-cited weakness of self-attention. Experimental results on WMT14 English-to-German and NIST Chinese-to-English translation tasks show the proposed approach consistently improves performance. Targeted linguistic analysis reveals that Mg-Sa indeed captures useful ... : EMNLP 2019 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1909.02222 https://dx.doi.org/10.48550/arxiv.1909.02222
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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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