Does Structure Matter? Encoding Documents for Machine Reading Comprehension
Hui Wan, Song Feng, Chulaka Gunasekara, Siva Sankalp Patel, Sachindra Joshi, Luis Lastras
Abstract
Machine reading comprehension is a challenging task especially for querying documents with deep and interconnected contexts. Transformer-based methods have shown advanced performances on this task; however, most of them still treat documents as a flat sequence of tokens. This work proposes a new Transformer-based method that reads a document as tree slices. It contains two modules for identifying more relevant text passage and the best answer span respectively, which are not only jointly trained but also jointly consulted at inference time. Our evaluation results show that our proposed method outperforms several competitive baseline approaches on two datasets from varied domains.
BibTeX
@inproceedings{wan-etal-2021-structure,
title = "Does Structure Matter? Encoding Documents for Machine Reading Comprehension",
author = "Wan, Hui and
Feng, Song and
Gunasekara, Chulaka and
Patel, Siva Sankalp and
Joshi, Sachindra and
Lastras, Luis",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.367/",
doi = "10.18653/v1/2021.naacl-main.367",
pages = "4626--4634"
}