Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction

Hanqi Yan, Lin Gui, Gabriele Pergola, Yulan He


Abstract
The Emotion Cause Extraction (ECE) task aims to identify clauses which contain emotion-evoking information for a particular emotion expressed in text. We observe that a widely-used ECE dataset exhibits a bias that the majority of annotated cause clauses are either directly before their associated emotion clauses or are the emotion clauses themselves. Existing models for ECE tend to explore such relative position information and suffer from the dataset bias. To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses. We test the performance of existing models on such adversarial examples and observe a significant performance drop. To address the dataset bias, we propose a novel graph-based method to explicitly model the emotion triggering paths by leveraging the commonsense knowledge to enhance the semantic dependencies between a candidate clause and an emotion clause. Experimental results show that our proposed approach performs on par with the existing state-of-the-art methods on the original ECE dataset, and is more robust against adversarial attacks compared to existing models.
Anthology ID:
2021.acl-long.261
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:
3364–3375
Language:
URL:
https://aclanthology.org/2021.acl-long.261
DOI:
10.18653/v1/2021.acl-long.261
Bibkey:
Cite (ACL):
Hanqi Yan, Lin Gui, Gabriele Pergola, and Yulan He. 2021. Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction. 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 3364–3375, Online. Association for Computational Linguistics.
Cite (Informal):
Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction (Yan et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.261.pdf
Video:
 https://aclanthology.org/2021.acl-long.261.mp4
Code
 hanqi-qi/Position-Bias-Mitigation-in-Emotion-Cause-Analysis
Data
ConceptNet