ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning

Swarnadeep Saha, Prateek Yadav, Lisa Bauer, Mohit Bansal


Abstract
Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context. Discriminative tasks are limiting because they fail to adequately evaluate the model’s ability to reason and explain predictions with underlying commonsense knowledge. They also allow such models to use reasoning shortcuts and not be “right for the right reasons”. In this work, we present ExplaGraphs, a new generative and structured commonsense-reasoning task (and an associated dataset) of explanation graph generation for stance prediction. Specifically, given a belief and an argument, a model has to predict if the argument supports or counters the belief and also generate a commonsense-augmented graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. We collect explanation graphs through a novel Create-Verify-And-Refine graph collection framework that improves the graph quality (up to 90%) via multiple rounds of verification and refinement. A significant 79% of our graphs contain external commonsense nodes with diverse structures and reasoning depths. Next, we propose a multi-level evaluation framework, consisting of automatic metrics and human evaluation, that check for the structural and semantic correctness of the generated graphs and their degree of match with ground-truth graphs. Finally, we present several structured, commonsense-augmented, and text generation models as strong starting points for this explanation graph generation task, and observe that there is a large gap with human performance, thereby encouraging future work for this new challenging task.
Anthology ID:
2021.emnlp-main.609
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7716–7740
Language:
URL:
https://aclanthology.org/2021.emnlp-main.609
DOI:
10.18653/v1/2021.emnlp-main.609
Bibkey:
Cite (ACL):
Swarnadeep Saha, Prateek Yadav, Lisa Bauer, and Mohit Bansal. 2021. ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7716–7740, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning (Saha et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.609.pdf
Code
 swarnaHub/ExplaGraphs