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1
Improving Pre-trained Language Models with Syntactic Dependency Prediction Task for Chinese Semantic Error Recognition ...
Sun, Bo; Wang, Baoxin; Che, Wanxiang. - : arXiv, 2022
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2
ExpMRC: explainability evaluation for machine reading comprehension
In: Heliyon (2022)
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3
Multilingual multi-aspect explainability analyses on machine reading comprehension models
In: iScience (2022)
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4
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models ...
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5
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL ...
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6
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling ...
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7
A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples ...
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8
Dynamic Connected Networks for Chinese Spelling Check ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.216 Abstract: Chinese spelling check (CSC) is a task to detect and correct spelling errors in Chinese text. Most state-of-the-art works on the CSC task adopt a BERT-based non-autoregressive language model, which relies on the output independence assumption. The inappropriate independence assumption prevents BERT-based models from learning the dependencies among target tokens, resulting in an incoherent problem. To address the above issue, we propose a novel architecture named Dynamic Connected Networks (DCN), which generates the candidate Chinese characters via a Pinyin Enhanced Candidate Generator and then utilizes an attention-based network to model the dependencies between two adjacent Chinese characters. The experimental results show that our proposed method achieves a new state-of-the-art performance on three human-annotated datasets. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/26307-dynamic-connected-networks-for-chinese-spelling-check
https://dx.doi.org/10.48448/k2y4-6q18
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9
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling ...
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10
Neural Stylistic Response Generation with Disentangled Latent Variables ...
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11
Guided Generation of Cause and Effect ...
Li, Zhongyang; Ding, Xiao; Liu, Ting. - : arXiv, 2021
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12
Language learners' enjoyment and emotion regulation in online collaborative learning
Zhang, Zhipeng (S34844); Liu, Ting; Lee, Chweebeng (R17032). - : U.K., Elsevier, 2021
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13
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching ...
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14
Cross-Lingual Machine Reading Comprehension ...
Cui, Yiming; Che, Wanxiang; Liu, Ting. - : arXiv, 2019
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15
CoNLL 2018 Shared Task System Outputs
Zeman, Daniel; Potthast, Martin; Duthoo, Elie. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018
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16
CoNLL 2017 Shared Task System Outputs
Zeman, Daniel; Potthast, Martin; Straka, Milan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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17
Modeling Leadership Behavior of Players in Virtual Worlds
In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment; Vol. 11 No. 1 (2015): Eleventh Artificial Intelligence and Interactive Digital Entertainment Conference ; 2334-0924 ; 2326-909X (2015)
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18
Question Popularity Analysis and Prediction in Community Question Answering Services
Liu, Ting; Zhang, Wei-Nan; Cao, Liujuan. - : Public Library of Science, 2014
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19
Modeling Sociocultural phenomena in discourse
In: Natural language engineering. - Cambridge : Cambridge University Press 19 (2013) 2, 213-257
OLC Linguistik
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20
Phrasal Paraphrase Based Question Reformulation for Archived Question Retrieval
Zhang, Yu; Zhang, Wei-Nan; Lu, Ke. - : Public Library of Science, 2013
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