Conditional Causal Relationships between Emotions and Causes in Texts

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 3111–3121, November 16–20, 2020. c⃝2020 Association for Computational Linguistics 3111 Conditional Causal Relationships between Emotions and Causes in Texts Xinhong Chen1, Qing Li2, Jianping Wang1 1 Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong 2 Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong xinhchen2-c@my.cityu.edu.hk, jianwang@cityu.edu.hk qing-prof.li@polyu.edu.hk Abstract The causal relationships between emotions and causes in text have recently received a lot of attention. Most of the existing works fo- cus 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 ex- tracted 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 relation- ship 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 com- putational overhead to fine-tune the prediction results based on the characteristics of the in- put clauses. Experiments demonstrate the ef- fectiveness and generality of our aggregation module. 1 Introduction Recently, the research on the causal relationships between human emotions and their corresponding causes has received much attention. Recognizing the causes of a specific emotion in a document is considered as more useful than only identifying the emotion, due to the great potential of helping people make reasonable decisions and avoid unnec- essary loss (Gui et al., 2017; Li et al., 2018; Xia et al., 2019; Ding et al., 2019). There are currently two well-designed tasks con- cerning the causal relationships between emotions and their causes, the Emotion Cause Extraction (ECE) task (Lee et al., 2010; Chen et al., 2010; Gui et al., 2016a,b) and the Emotion-Cause Pair Extraction (ECPE) task (Xia and Ding, 2019; Chen et al., 2018). Specifically, the ECE task focuses on extracting the causes for a given emotion, while the ECPE task focuses on extracting emotions and the corresponding causes as pairs. Despite their increasing popularity, both tasks only aim to extract the clauses containing causal re- lationships, and have neglected the possibility that the context clauses may be indispensable for the extracted clauses to have a valid causal relationship. Let us consider the following example: • Wu was diagnosed with advanced liver cancer at the beginning of 2014, • since when he began to update his health condition in Microblog and has attracted much attention from many users. • If Wu didn't update his microblog for a long time, • people worried that he may have passed away. • There was one time that Wu hadn't updated his microblog for about two months, • and he had received a lot of messages concerned about his health conditions. • ... Figure …

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