Emotion-cause pair extraction based on interactive attention

Huang, Weichun; Yang, Yixue; Huang, Xiaohui; Peng, Zhiying; Li-yan, Xiong

doi:10.1007/s10489-022-03873-x

Cited by 4 publications

(2 citation statements)

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“…Studies on ECPE [ 15 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ] have solved this problem by dividing it into emotion and cause extraction stages to extract emotions and their causes. When finding the emotion–cause pair in a two-step process, the emotion is extracted in the first step, the cause is extracted in the next step, and the method of connecting the extracted cause and the result of the emotion differs according to the specific study.…”

Section: Related Workmentioning

confidence: 99%

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A Token Classification-Based Attention Model for Extracting Multiple Emotion–Cause Pairs in Conversations

Yoo

Jeong

2023

Sensors

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People exchange emotions through conversations with others and provide different answers depending on the reasons for their emotions. During a conversation, it is important to find not only such emotions but also their cause. Emotion–cause pair extraction (ECPE) is a task used to determine emotions and their causes in a single pair within a text, and various studies have been conducted to accomplish ECPE tasks. However, existing studies have limitations in that some models conduct the task in two or more steps, whereas others extract only one emotion–cause pair for a given text. We propose a novel methodology for extracting multiple emotion–cause pairs simultaneously from a given conversation with a single model. Our proposed model is a token-classification-based emotion–cause pair extraction model, which applies the BIO (beginning–inside–outside) tagging scheme to efficiently extract multiple emotion–cause pairs in conversations. The proposed model showed the best performance on the RECCON benchmark dataset in comparative experiments with existing studies and was experimentally verified to efficiently extract multiple emotion–cause pairs in conversations.

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Section: Related Workmentioning

confidence: 99%

“…First, most models are inefficiently constructed. Many studies [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ] have proposed end-to-end models that take several steps to pair emotions and their causes. Emotions are extracted using an emotion extraction model, and the cause is extracted using an extraction model.…”

Section: Introductionmentioning

confidence: 99%