Understand before Answer: Improve Temporal Reading Comprehension via Precise Question UnderstandingDownload PDF

Anonymous

08 Mar 2022 (modified: 05 May 2023)NAACL 2022 Conference Blind SubmissionReaders: Everyone
Paper Link: https://openreview.net/forum?id=D0zGt8jxCIl
Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)
Abstract: This work studies temporal reading comprehension (TRC), which reads a free-text passage and answers temporal ordering questions. Precise question understanding is critical for temporal reading comprehension. For example, the question "What happened before the victory" and "What happened after the victory" share almost all words except one, while their answers are totally different. Moreover, even if two questions query about similar temporal relations, different varieties might also lead to various answers. For example, although both the question "What usually happened during the press release?" and "What might happen during the press release" query events which happen after "the press release", they convey divergent semantics. To this end, we propose a novel reading comprehension approach with precise question understanding. Specifically, a temporal ordering question is embedded into two vectors to capture the referred event and the temporal relation. Then we evaluate the temporal relation between candidate events and the referred event based on that. Such fine-grained representations offer two benefits. First, it enables a better understanding of the question by focusing on different elements of a question. Second, it provides good interpretability when evaluating temporal relations. Furthermore, we also harness an auxiliary contrastive loss for representation learning of temporal relations, which aims to distinguish relations with subtle but critical changes. The proposed approach outperforms strong baselines and achieves state-of-the-art performance on the TORQUE dataset. It also increases the accuracy of four pre-trained language models (BERT base, BERT large, RoBERTa base, and RoBETRa large), demonstrating its generic effectiveness on divergent models.
Presentation Mode: This paper will be presented virtually
Virtual Presentation Timezone: UTC+8
Copyright Consent Signature (type Name Or NA If Not Transferrable): Hao Huang
Copyright Consent Name And Address: Australian AI Institute, School of CS, FEIT, University of Technology Sydney, 15 Broadway, Ultimo NSW 2007, Sydney, Australia
0 Replies

Loading