DiscoSense: Commonsense Reasoning with Discourse Connectives

Prajjwal Bhargava, Vincent Ng


Abstract
We present DiscoSense, a benchmark for commonsense reasoning via understanding a wide variety of discourse connectives. We generate compelling distractors in DiscoSense using Conditional Adversarial Filtering, an extension of Adversarial Filtering that employs conditional generation. We show that state-of-the-art pre-trained language models struggle to perform well on DiscoSense, which makes this dataset ideal for evaluating next-generation commonsense reasoning systems.
Anthology ID:
2022.emnlp-main.703
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10295–10310
Language:
URL:
https://aclanthology.org/2022.emnlp-main.703
DOI:
10.18653/v1/2022.emnlp-main.703
Bibkey:
Cite (ACL):
Prajjwal Bhargava and Vincent Ng. 2022. DiscoSense: Commonsense Reasoning with Discourse Connectives. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10295–10310, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
DiscoSense: Commonsense Reasoning with Discourse Connectives (Bhargava & Ng, EMNLP 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.703.pdf