A Densely Connected Encoder Stack Approach for Multi-type Legal Machine Reading Comprehension
Paper
Paper/Presentation Title | A Densely Connected Encoder Stack Approach for Multi-type Legal Machine Reading Comprehension |
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Presentation Type | Paper |
Authors | Nai, Peiran (Author), Li, Lin (Author) and Tao, Xiaohui (Author) |
Editors | Huang, Zhisheng, Beek, Wouter, Wang, Hua, Zhou, Rui and Zhang, Yanchun |
Journal or Proceedings Title | Web Information Systems Engineering – WISE 2020: 21st International Conference, Amsterdam, The Netherlands, October 20–24, 2020, Proceedings, Part II |
ERA Conference ID | 43582 |
Number of Pages | 15 |
Year | 2020 |
Place of Publication | Cham, Switzerland |
ISBN | 9783030620073 |
9783030620080 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-62008-0_12 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-030-62008-0_12 |
Conference/Event | 21st International Conference on Web Information Systems Engineering (WISE 2020) |
International Conference on Web Information Systems Engineering | |
Event Details | International Conference on Web Information Systems Engineering WISE Rank A A A A A A A A A A A A A A A A A A |
Event Details | 21st International Conference on Web Information Systems Engineering (WISE 2020) Event Date 20 to end of 24 Oct 2020 Event Location Amsterdam, The Netherlands |
Abstract | Legal machine reading comprehension (MRC) is becoming increasingly important as the number of legal documents rapidly grows. Currently, the main approach of MRC is the deep neural network based model which learns multi-level semantic information with different granularities layer by layer, and it converts the original data from shallow features into abstract features. Owing to excessive abstract semantic features learned by the model at the top of layers and the large loss of shallow features, the current approach still can be strengthened when applying to the legal field. In order to solve the problem, this paper proposes a Densely Connected Encoder Stack Approach for Multi-type Legal MRC. It can easily get multi-scale semantic features. A novel loss function named multi-type loss is designed to enhance the legal MRC performance. In addition, our approach includes a bidirectional recurrent convolutional layer to learn local features and assist in answering general questions. And several fully connected layers are used to keep position features and make predictions. Both extensive experiments and ablation studies in the biggest Chinese legal dataset demonstrate the effectiveness of our approach. Finally, our approach achieves 0.817 in terms of F1 in CJRC dataset and 83.4 in the SQuAD2.0 dev. |
Keywords | Multi-type question answering; Legal machine reading comprehension; Dense encoder stack |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460208. Natural language processing | |
Byline Affiliations | Wuhan University of Technology, China |
School of Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5zq2/a-densely-connected-encoder-stack-approach-for-multi-type-legal-machine-reading-comprehension
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