Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension
Shaoru Guo, Yong Guan, Ru Li, Xiaoli Li, Hongye Tan
Abstract
Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding. It is surprising that jointly considering syntax and semantics in neural networks was never formally reported in literature. This paper makes the first attempt by proposing a novel Syntax and Frame Semantics model for Machine Reading Comprehension (SS-MRC), which takes full advantage of syntax and frame semantics to get richer text representation. Our extensive experimental results demonstrate that SS-MRC performs better than ten state-of-the-art technologies on machine reading comprehension task.
BibTeX
@inproceedings{guo-etal-2020-incorporating,
title = "Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension",
author = "Guo, Shaoru and
Guan, Yong and
Li, Ru and
Li, Xiaoli and
Tan, Hongye",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.237/",
doi = "10.18653/v1/2020.coling-main.237",
pages = "2635--2641"
}