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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 – 9 of 9 1 Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network ... Ren, Yuqi; Xiong, Deyi. - : arXiv, 2021 BASE Show details 2 RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling ... Quan, Jun; Zhang, Shian; Cao, Qian. - : arXiv, 2020 BASE Show details 3 Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ... Xu, Hongfei; van Genabith, Josef; Liu, Qiuhui. - : arXiv, 2020 BASE Show details 4 Modeling Homophone Noise for Robust Neural Machine Translation ... Qin, Wenjie; Li, Xiang; Sun, Yuhui. - : arXiv, 2020 BASE Show details 5 Merging External Bilingual Pairs into Neural Machine Translation ... Wang, Tao; Kuang, Shaohui; Xiong, Deyi. - : arXiv, 2019 BASE Show details 6 BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels ... Jing, Yimin; Xiong, Deyi; Zhen, Yan. - : arXiv, 2019 BASE Show details 7 Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model ... Kuang, Shaohui; Xiong, Deyi. - : arXiv, 2018 BASE Show details 8 Modeling Source Syntax for Neural Machine Translation ... Li, Junhui; Xiong, Deyi; Tu, Zhaopeng; Zhu, Muhua; Zhang, Min; Zhou, Guodong. - : arXiv, 2017 Abstract: Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we linearize parse trees of source sentences to obtain structural label sequences. On the basis, we propose three different sorts of encoders to incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word and label annotation vectors parallelly; 2) Hierarchical RNN encoder that learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed RNN encoder that stitchingly learns word and label annotation vectors over sequences where words and labels are mixed. Experimentation on Chinese-to-English translation demonstrates that all the three proposed syntactic encoders are able to improve translation accuracy. It is interesting to note that the simplest RNN encoder, ... : Accepted by ACL 2017 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1705.01020 https://dx.doi.org/10.48550/arxiv.1705.01020 BASE Hide details 9 BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings ... Zhang, Biao; Xiong, Deyi; Su, Jinsong. - : arXiv, 2016 BASE Show details

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