Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction
Bowen Xing Ivor W. Tsang
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.