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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 – 5 of 5 1 G-Transformer for Document-Level Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Boxing; Bao, Guangsheng. - : Underline Science Inc., 2021 BASE Show details 2 End-to-End Chinese Parsing Exploiting Lexicons ... Zhang, Yuan; Teng, Zhiyang; Zhang, Yue. - : arXiv, 2020 BASE Show details 3 Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning Guo, Zhijiang; Zhang, Yan; Teng, Zhiyang... In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 297-312 (2019) (2019) BASE Show details 4 Combining Discrete and Neural Features for Sequence Labeling ... Yang, Jie; Teng, Zhiyang; Zhang, Meishan; Zhang, Yue. - : arXiv, 2017 Abstract: Neural network models have recently received heated research attention in the natural language processing community. Compared with traditional models with discrete features, neural models have two main advantages. First, they take low-dimensional, real-valued embedding vectors as inputs, which can be trained over large raw data, thereby addressing the issue of feature sparsity in discrete models. Second, deep neural networks can be used to automatically combine input features, and including non-local features that capture semantic patterns that cannot be expressed using discrete indicator features. As a result, neural network models have achieved competitive accuracies compared with the best discrete models for a range of NLP tasks. On the other hand, manual feature templates have been carefully investigated for most NLP tasks over decades and typically cover the most useful indicator pattern for solving the problems. Such information can be complementary the features automatically induced from neural ... : Accepted by International Conference on Computational Linguistics and Intelligent Text Processing (CICLing) 2016, April ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1708.07279 https://arxiv.org/abs/1708.07279 BASE Hide details 5 Bidirectional Tree-Structured LSTM with Head Lexicalization ... Teng, Zhiyang; Zhang, Yue. - : arXiv, 2016 BASE Show details

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