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Unifying emotion-oriented and cause-oriented predictions for emotion-cause extraction
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AI
DOI：10.1016/j.neunet.2024.106431.png)
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Abstract
En 中文
Emotion -cause pair extraction (ECPE) is an extraction task aiming to simultaneously identify the emotions and causes from the text without emotion annotations. Let c i and c j represent the emotion clause and the cause clause of a document, respectively, and we can predict one from the other and vice versa. Previous works fail to take advantage of this bidirectional opportunity. We refer to the prediction from c i to c j , i.e., c i -* c j , as an emotion -oriented cause prediction (EoCP) task and the prediction from c j to c i , i.e., c j -* c i , as a cause -oriented emotion prediction (CoEP) task. After redefining the ECPE task, we propose a novel unified architecture for ECPE, which incorporates EoCP and CoEP as cells and unifies them into a single -chain architecture. Additionally, we redefine emotion -cause pair extraction as a closed -loop structure detection problem to alleviate the mismatch between emotion and cause clauses. To enhance the training of the architecture, we provide a procedure for estimating the confidence of the extraction system for its emotion -cause pairs. We demonstrate the superiority of our proposed model through extensive experiments on two public datasets, achieving a new state-of-the-art performance. Furthermore, our method particularly achieves significant improvements in multiple emotion -cause pair extraction.
Keywords:
Emotion-cause pair extraction
Sentiment analysis
Emotion cause analysis
Bidirectional extraction system
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