Work

Dialogue relation extraction based on Multi-Perspective Contextual Interaction Attention Network

Year: 2025

Type: article

Abstract: Dialogue relation extraction aims to identify semantic relations between entity pairs from dialogue text. Multi-party dialogues present unique challenges due to dynamic speaker interactions and cross-turn dependencies, which existing methods fail to adequately model. We propose the Multi-Perspective Contextual Interaction Attention Network (MPCIA-Net) to address these limitations through two key components: (1) The Dual-stream Context Disentanglement Module ( DCDM ), which decouples context representations from the base encoder through parallel feature streams and integrates fine-grained contextual features; (2) The Dual-Attention Fusion Module ( DAF ), which processes features from dual perspectives—speaker perception for pronoun resolution through dialogue participant identity modelling, and utterance perception for capturing long-range cross-turn dependencies. The representations from both channels are merged through an adaptive attention mechanism for final relation prediction. Experimental results demonstrate that MPCIA-Net achieves an F 1 score of 68.70% on the DialogRE dataset, surpassing current state-of-the-art methods.

Source: Egyptian Informatics Journal

DOI: 10.1016/j.eij.2025.100842