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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation ... Hao, Yongchang; He, Shilin; Jiao, Wenxiang. - : arXiv, 2020 BASE Show details 2 Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models ... He, Shilin; Wang, Xing; Shi, Shuming. - : arXiv, 2020 BASE Show details 3 Towards Understanding Neural Machine Translation with Word Importance ... He, Shilin; Tu, Zhaopeng; Wang, Xing; Wang, Longyue; Lyu, Michael R.; Shi, Shuming. - : arXiv, 2019 Abstract: Although neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, the interpretability of NMT remains unsatisfactory. In this work, we propose to address this gap by focusing on understanding the input-output behavior of NMT models. Specifically, we measure the word importance by attributing the NMT output to every input word through a gradient-based method. We validate the approach on a couple of perturbation operations, language pairs, and model architectures, demonstrating its superiority on identifying input words with higher influence on translation performance. Encouragingly, the calculated importance can serve as indicators of input words that are under-translated by NMT models. Furthermore, our analysis reveals that words of certain syntactic categories have higher importance while the categories vary across language pairs, which can inspire better design principles of NMT architectures for multi-lingual translation. ... : EMNLP 2019 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.1909.00326 https://arxiv.org/abs/1909.00326 BASE Hide details

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