Coreference Reasoning in Machine Reading Comprehension

Mingzhu Wu, Nafise Sadat Moosavi, Dan Roth, Iryna Gurevych


Abstract
Coreference resolution is essential for natural language understanding and has been long studied in NLP. In recent years, as the format of Question Answering (QA) became a standard for machine reading comprehension (MRC), there have been data collection efforts, e.g., Dasigi et al. (2019), that attempt to evaluate the ability of MRC models to reason about coreference. However, as we show, coreference reasoning in MRC is a greater challenge than earlier thought; MRC datasets do not reflect the natural distribution and, consequently, the challenges of coreference reasoning. Specifically, success on these datasets does not reflect a model’s proficiency in coreference reasoning. We propose a methodology for creating MRC datasets that better reflect the challenges of coreference reasoning and use it to create a sample evaluation set. The results on our dataset show that state-of-the-art models still struggle with these phenomena. Furthermore, we develop an effective way to use naturally occurring coreference phenomena from existing coreference resolution datasets when training MRC models. This allows us to show an improvement in the coreference reasoning abilities of state-of-the-art models.
Anthology ID:
2021.acl-long.448
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5768–5781
Language:
URL:
https://aclanthology.org/2021.acl-long.448
DOI:
10.18653/v1/2021.acl-long.448
Bibkey:
Cite (ACL):
Mingzhu Wu, Nafise Sadat Moosavi, Dan Roth, and Iryna Gurevych. 2021. Coreference Reasoning in Machine Reading Comprehension. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5768–5781, Online. Association for Computational Linguistics.
Cite (Informal):
Coreference Reasoning in Machine Reading Comprehension (Wu et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.acl-long.448.pdf
Optional supplementary material:
 2021.acl-long.448.OptionalSupplementaryMaterial.zip
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.acl-long.448.mp4
Code
 UKPLab/coref-reasoning-in-qa
Data
QuorefSQuAD