A multi-task learning machine reading comprehension model for noisy document (student abstract)

Zhijing Wu, Hua Xu*

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Current neural models for Machine Reading Comprehension (MRC) have achieved successful performance in recent years. However, the model is too fragile and lack robustness to tackle the imperceptible adversarial perturbations to the input. In this work, we propose a multi-task learning MRC model with a hierarchical knowledge enrichment to further improve the robustness for noisy document. Our model follows a typical encode-align-decode framework. Additionally, we apply a hierarchical method of adding background knowledge into the model from coarse-to-fine to enhance the language representations. Besides, we optimize our model by jointly training the answer span and unanswerability prediction, aiming to improve the robustness to noise. Experiment results on benchmark datasets confirm the superiority of our method, and our method can achieve competitive performance compared with other strong baselines.

源语言英语
主期刊名AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
出版商AAAI press
13963-13964
页数2
ISBN(电子版)9781577358350
出版状态已出版 - 2020
已对外发布
活动34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, 美国
期限: 7 2月 202012 2月 2020

出版系列

姓名AAAI 2020 - 34th AAAI Conference on Artificial Intelligence

会议

会议34th AAAI Conference on Artificial Intelligence, AAAI 2020
国家/地区美国
New York
时期7/02/2012/02/20

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引用此

Wu, Z., & Xu, H. (2020). A multi-task learning machine reading comprehension model for noisy document (student abstract). 在 AAAI 2020 - 34th AAAI Conference on Artificial Intelligence (页码 13963-13964). (AAAI 2020 - 34th AAAI Conference on Artificial Intelligence). AAAI press.