Related papers: Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction

Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction

URL: http://arxiv.org/abs/2306.04340v1
Date: Wed, 7 Jun 2023 11:11:12 GMT
Title: Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction
Authors: Bowen Xing and Ivor W. Tsang
Abstract summary: We propose a new MTL framework based on Co-evolving Reasoning. We show that our model achieves new state-of-the-art performance.
Score: 39.76268402567324
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Emotion-Cause Pair Extraction (ECPE) aims to extract all emotion clauses and their corresponding cause clauses from a document. Existing approaches tackle this task through multi-task learning (MTL) framework in which the two subtasks provide indicative clues for ECPE. However, the previous MTL framework considers only one round of multi-task reasoning and ignores the reverse feedbacks from ECPE to the subtasks. Besides, its multi-task reasoning only relies on semantics-level interactions, which cannot capture the explicit dependencies, and both the encoder sharing and multi-task hidden states concatenations can hardly capture the causalities. To solve these issues, we first put forward a new MTL framework based on Co-evolving Reasoning. It (1) models the bidirectional feedbacks between ECPE and its subtasks; (2) allows the three tasks to evolve together and prompt each other recurrently; (3) integrates prediction-level interactions to capture explicit dependencies. Then we propose a novel multi-task relational graph (MRG) to sufficiently exploit the causal relations. Finally, we propose a Co-evolving Graph Reasoning Network (CGR-Net) that implements our MTL framework and conducts Co-evolving Reasoning on MRG. Experimental results show that our model achieves new state-of-the-art performance, and further analysis confirms the advantages of our method.

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