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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; Zheng, Bo; Liu, Ting. - : arXiv, 2018 Abstract: This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanford's winning system for the CoNLL 2017 shared task and make two effective extensions: 1) incorporating deep contextualized word embeddings into both the part of speech tagger and parser; 2) ensembling parsers trained with different initialization. We also explore different ways of concatenating treebanks for further improvements. Experimental results on the development data show the effectiveness of our methods. In the final evaluation, our system was ranked first according to LAS (75.84%) and outperformed the other systems by a large margin. ... : System description paper of our system (HIT-SCIR) for the CoNLL 2018 shared task on Universal Dependency parsing, which was ranked first in the LAS evaluation. Fix typos and grammar errors. Add the results of parser without ensemble ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1807.03121 https://arxiv.org/abs/1807.03121 BASE Hide details 5 Neural Personalized Response Generation as Domain Adaptation ... Zhang, Weinan; Liu, Ting; Wang, Yifa. - : arXiv, 2017 BASE Show 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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