An End-to-End Network for Emotion-Cause Pair Extraction
This work addresses the problem of automatically extracting emotion-cause pairs from text without requiring emotion annotations, which is incremental as it builds on prior ECE and ECPE research.
The paper tackles the task of Emotion-Cause Pair Extraction (ECPE) by proposing an end-to-end model, which achieves a ~6.5 increase in F1 score over multi-stage approaches and comparable performance to state-of-the-art methods on an adapted NTCIR-13 dataset.
The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential clause-pairs of emotions and their corresponding causes in a document. Unlike the more well-studied task of Emotion Cause Extraction (ECE), ECPE does not require the emotion clauses to be provided as annotations. Previous works on ECPE have either followed a multi-stage approach where emotion extraction, cause extraction, and pairing are done independently or use complex architectures to resolve its limitations. In this paper, we propose an end-to-end model for the ECPE task. Due to the unavailability of an English language ECPE corpus, we adapt the NTCIR-13 ECE corpus and establish a baseline for the ECPE task on this dataset. On this dataset, the proposed method produces significant performance improvements (~6.5 increase in F1 score) over the multi-stage approach and achieves comparable performance to the state-of-the-art methods.