期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：2895-2906
DOI：10.18653/v1/2021.eacl-main.253
语种：English
出版社：ACL Anthology
摘要：Tables in web documents are pervasive and can be directly used to answer many of the queries searched on the web, motivating their integration in question answering. Very often information presented in tables is succinct and hard to interpret with standard language representations. On the other hand, tables often appear within textual context, such as an article describing the table. Using the information from an article as additional context can potentially enrich table representations. In this work we aim to improve question answering from tables by refining table representations based on information from surrounding text. We also present an effective method to combine text and table-based predictions for question answering from full documents, obtaining significant improvements on the Natural Questions dataset (Kwiatkowski et al., 2019).
