Paper Info
Title
Syntax-Aware Multi-Spans Generation for Reading Comprehension
Abstract
AbstractThis paper presents a novel method to generate answers for non-extraction machine reading comprehension (MRC) tasks whose answers cannot be simply extracted as one span from the given passages. Using a pointer network-style extractive decoder for such type of MRC may result in unsatisfactory performance when the ground-truth answers are given by human annotators or highly re-paraphrased from parts of the passages. On the other hand, using a generative decoder cannot well guarantee the resulted answers with well-formed syntax and semantics when encountering long sentences. Therefore, to alleviate the obvious drawbacks of both sides, we propose an answer making-up method from extracted multi-spans that are learned by our model as highly confident $n$-gram candidates in the given passage. That is, the returned answers are composed of discontinuous multi-spans but not just one consecutive span in the given passages anymore. The proposed method is simple but effective: empirical experiments on MS MARCO show that the proposed method has a better performance on accurately generating long answers and substantially outperforms two typical competitive one-span and Seq2Seq baseline decoders.
Year
DOI
Venue
2022
10.1109/TASLP.2021.3138679
IEEE/ACM Transactions on Audio, Speech and Language Processing
Keywords
DocType
Volume
Answer generation, encoder-decoder mechanism, machine reading comprehension, syntactic parsing
Journal
10.5555
Issue
ISSN
Citations 
taslp.2022.issue-30
2329-9290
0
PageRank 
References 
Authors
0.34
12
3
Name
Order
Citations
PageRank
Zhuosheng Zhang15714.93
Yiqing Zhang2799.74
Hai Zhao3960113.64