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| Label-Enhanced Reading Comprehension Model |
| SU Lixin1,2, GUO Jiafeng1,2, FAN Yixing1, LAN Yanyan1,2, CHENG Xueqi3 |
1. Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190;
2. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100190;
3. Institute of Network Technology, Institute of Computing Technology (YANTAI), Chinese Academy of Sciences, Yantai 264005 |
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Abstract In the existing extractive reading comprehension models, only the boundary of answers is utilized as the supervision signal and the labeling processed by human is ignored. Consequently, learned models are prone to learn the superficial features and the generalization performance is degraded. In this paper, a label-enhanced reading comprehension model is proposed to imitate human activity. The answer-bearing sentence, the content and the boundary of the answer are learned simultaneously. The answer-bearing sentence and the content of the answer can be derived from the boundary of the answer and these three types of labels are regarded as supervision signals. The model is trained by multitask learning. During prediction, the probabilities from three predictions are merged to determine the answer, and thus the generalization performance is improved. Experiments on SQuAD dataset demonstrate the effectiveness of LE-Reader model.
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Received: 18 October 2019
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| Fund:Supported by National Key Research and Development Program of China(No.2016QY02D0405), National Natural Science Foundation of China(No.61425016,61722211,61773362,61872338,61902381), Youth Innovation Promotion Association of CAS(No.20144310,2016102), Foundation and Frontier Research Key Program of Chongqing Science and Technology Commission(No.cstc2017jcyjBX0059), Taishan Scholars Program of Shandong Province(No.ts201511082) |
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Corresponding Authors:
GUO Jiafeng, Ph.D., professor. His research interests include data mining and information retrieval.
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| About author:: SU Lixin, Ph.D. candidate. His research interests include information retrieval and question answering; FAN Yixing, Ph.D., research assistant. His research interests include data mining and information retrieval; LAN Yanyan, Ph.D., associate professor. Her research interests include machine lear-ning and data mining; CHENG Xueqi, Ph.D., professor. His research interests include network science and social computing, web search and data mi-ning. |
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2020, Vol. 33 
