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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 – 10 of 10 1 Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training ... Zheng, Bo; Dong, Li; Huang, Shaohan. - : arXiv, 2021 BASE Show details 2 Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network ... Jiang, Tianwen; Zhao, Tong; Qin, Bing. - : arXiv, 2020 BASE Show details 3 N-LTP: An Open-source Neural Language Technology Platform for Chinese ... Che, Wanxiang; Feng, Yunlong; Qin, Libo. - : arXiv, 2020 BASE Show details 4 Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation ... Che, Wanxiang; Liu, Yijia; Wang, Yuxuan. - : arXiv, 2018 BASE Show details 5 Neural Personalized Response Generation as Domain Adaptation ... Zhang, Weinan; Liu, Ting; Wang, Yifa; Zhu, Qingfu. - : arXiv, 2017 Abstract: In this paper, we focus on the personalized response generation for conversational systems. Based on the sequence to sequence learning, especially the encoder-decoder framework, we propose a two-phase approach, namely initialization then adaptation, to model the responding style of human and then generate personalized responses. For evaluation, we propose a novel human aided method to evaluate the performance of the personalized response generation models by online real-time conversation and offline human judgement. Moreover, the lexical divergence of the responses generated by the 5 personalized models indicates that the proposed two-phase approach achieves good results on modeling the responding style of human and generating personalized responses for the conversational systems. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1701.02073 https://arxiv.org/abs/1701.02073 BASE Hide details 6 Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning ... Guo, Jiang; Che, Wanxiang; Wang, Haifeng. - : arXiv, 2016 BASE Show details 7 Additional file 1: of Prediction of the next highly pathogenic avian influenza pandemic that can cause illness in humans ... Zhang, Zhong-Wei; Liu, Ting; Zeng, Jian. - : Figshare, 2015 BASE Show details 8 Additional file 1: of Prediction of the next highly pathogenic avian influenza pandemic that can cause illness in humans ... Zhang, Zhong-Wei; Liu, Ting; Zeng, Jian. - : Figshare, 2015 BASE Show details 9 Effective LSTMs for Target-Dependent Sentiment Classification ... Tang, Duyu; Qin, Bing; Feng, Xiaocheng. - : arXiv, 2015 BASE Show details 10 Emotion Analysis Platform on Chinese Microblog ... Tang, Duyu; Qin, Bing; Liu, Ting. - : arXiv, 2014 BASE Show details

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