Open Access

ARTICLE

Dialogue Relation Extraction Enhanced with Trigger: A Multi-Feature Filtering and Fusion Model

Haitao Wang^1,2, Yuanzhao Guo^1,2, Xiaotong Han^1,2, Yuan Tian^1,2,*

1 School of Artificial Intelligence, Jilin University, Changchun, 130012, China
2 Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, Changchun, 130012, China

* Corresponding Author: Yuan Tian. Email:

Computers, Materials & Continua 2025, 83(1), 137-155. https://doi.org/10.32604/cmc.2025.060534

Received 04 November 2024; Accepted 17 January 2025; Issue published 26 March 2025

Abstract

Relation extraction plays a crucial role in numerous downstream tasks. Dialogue relation extraction focuses on identifying relations between two arguments within a given dialogue. To tackle the problem of low information density in dialogues, methods based on trigger enhancement have been proposed, yielding positive results. However, trigger enhancement faces challenges, which cause suboptimal model performance. First, the proportion of annotated triggers is low in DialogRE. Second, feature representations of triggers and arguments often contain conflicting information. In this paper, we propose a novel Multi-Feature Filtering and Fusion trigger enhancement approach to overcome these limitations. We first obtain representations of arguments, and triggers that contain rich semantic information through attention and gate methods. Then, we design a feature filtering mechanism that eliminates conflicting features in the encoding of trigger prototype representations and their corresponding argument pairs. Additionally, we utilize large language models to create prompts based on Chain-of-Thought and In-context Learning for automated trigger extraction. Experiments show that our model increases the average F1 score by 1.3% in the dialogue relation extraction task. Ablation and case studies confirm the effectiveness of our model. Furthermore, the feature filtering method effectively integrates with other trigger enhancement models, enhancing overall performance and demonstrating its ability to resolve feature conflicts.

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Keywords

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