MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension

Huaishao Luo, Yu Shi, Ming Gong, Linjun Shou, Tianrui Li


Abstract
Span extraction is an essential problem in machine reading comprehension. Most of the existing algorithms predict the start and end positions of an answer span in the given corresponding context by generating two probability vectors. In this paper, we propose a novel approach that extends the probability vector to a probability matrix. Such a matrix can cover more start-end position pairs. Precisely, to each possible start index, the method always generates an end probability vector. Besides, we propose a sampling-based training strategy to address the computational cost and memory issue in the matrix training phase. We evaluate our method on SQuAD 1.1 and three other question answering benchmarks. Leveraging the most competitive models BERT and BiDAF as the backbone, our proposed approach can get consistent improvements in all datasets, demonstrating the effectiveness of the proposed method.
Anthology ID:
2020.aacl-main.69
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Month:
December
Year:
2020
Address:
Suzhou, China
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
687–695
Language:
URL:
https://aclanthology.org/2020.aacl-main.69
DOI:
Bibkey:
Cite (ACL):
Huaishao Luo, Yu Shi, Ming Gong, Linjun Shou, and Tianrui Li. 2020. MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 687–695, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension (Luo et al., AACL 2020)
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PDF:
https://aclanthology.org/2020.aacl-main.69.pdf
Data
HotpotQANatural QuestionsNewsQA