A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction

Yilin Niu, Fangkai Jiao, Mantong Zhou, Ting Yao, Jingfang Xu, Minlie Huang


Abstract
Neural models have achieved great success on machine reading comprehension (MRC), many of which typically consist of two components: an evidence extractor and an answer predictor. The former seeks the most relevant information from a reference text, while the latter is to locate or generate answers from the extracted evidence. Despite the importance of evidence labels for training the evidence extractor, they are not cheaply accessible, particularly in many non-extractive MRC tasks such as YES/NO question answering and multi-choice MRC. To address this problem, we present a Self-Training method (STM), which supervises the evidence extractor with auto-generated evidence labels in an iterative process. At each iteration, a base MRC model is trained with golden answers and noisy evidence labels. The trained model will predict pseudo evidence labels as extra supervision in the next iteration. We evaluate STM on seven datasets over three MRC tasks. Experimental results demonstrate the improvement on existing MRC models, and we also analyze how and why such a self-training method works in MRC.
Anthology ID:
2020.acl-main.361
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3916–3927
Language:
URL:
https://aclanthology.org/2020.acl-main.361
DOI:
10.18653/v1/2020.acl-main.361
Bibkey:
Cite (ACL):
Yilin Niu, Fangkai Jiao, Mantong Zhou, Ting Yao, Jingfang Xu, and Minlie Huang. 2020. A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3916–3927, Online. Association for Computational Linguistics.
Cite (Informal):
A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction (Niu et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.361.pdf
Video:
 http://slideslive.com/38928873
Code
 SparkJiao/Self-Training-MRC
Data
BoolQCoQAMultiRCQUASARQUASAR-TRACE