Conditional Causal Relationships between Emotions and Causes in Texts

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing
EditorsBonnie Webber, Trevor Cohn, Yulan He, Yang Li
PublisherAssociation for Computational Linguistics
Pages3111-3121
ISBN (Print)9781952148606
Publication statusPublished - Nov 2020

Publication series

NameEMNLP - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Conference

Title2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)
LocationVirtual
Period16 - 20 November 2020

Abstract

The causal relationships between emotions and causes in text have recently received a lot of attention. Most of the existing works focus on the extraction of the causally related clauses from documents. However, none of these works has considered the possibility that the causal relationships among the extracted emotion and cause clauses may only be valid under a specific context, without which the extracted clauses may not be causally related. To address such an issue, we propose a new task of determining whether or not an input pair of emotion and cause has a valid causal relationship under different contexts, and construct a corresponding dataset via manual annotation and negative sampling based on an existing benchmark dataset. Furthermore, we propose a prediction aggregation module with low computational overhead to fine-tune the prediction results based on the characteristics of the input clauses. Experiments demonstrate the effectiveness and generality of our aggregation module.

Citation Format(s)

Conditional Causal Relationships between Emotions and Causes in Texts. / Chen, Xinhong; Li, Qing; Wang, Jianping.

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. ed. / Bonnie Webber; Trevor Cohn; Yulan He; Yang Li. Association for Computational Linguistics, 2020. p. 3111-3121 (EMNLP - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review