Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization

Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, Kartik Talamadupula


Abstract
Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled. In this work, we use the multiple-choice reading comprehension (MCRC) and checking factual correctness of textual summarization (CFCS) tasks to investigate potential reasons for this. Our findings show that: (1) the relatively shorter length of premises in traditional NLI datasets is the primary challenge prohibiting usage in downstream applications (which do better with longer contexts); (2) this challenge can be addressed by automatically converting resource-rich reading comprehension datasets into longer-premise NLI datasets; and (3) models trained on the converted, longer-premise datasets outperform those trained using short-premise traditional NLI datasets on downstream tasks primarily due to the difference in premise lengths.
Anthology ID:
2021.naacl-main.104
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1322–1336
Language:
URL:
https://aclanthology.org/2021.naacl-main.104
DOI:
10.18653/v1/2021.naacl-main.104
Bibkey:
Cite (ACL):
Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, and Kartik Talamadupula. 2021. Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1322–1336, Online. Association for Computational Linguistics.
Cite (Informal):
Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization (Mishra et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.104.pdf
Video:
 https://aclanthology.org/2021.naacl-main.104.mp4
Data
ANLICosmosQADREAMMultiNLIMultiRCRACESNLI