Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information

• URL: http://arxiv.org/abs/2310.05073v1
• Date: Sun, 8 Oct 2023 08:47:10 GMT
• Title: Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information
• Authors: Yun Luo and Zhen Yang and Fandong Meng and Yingjie Li and Jie Zhou and Yue Zhang
• Abstract summary: We propose an Efficient Context-aware model (ECASE) that fully exploits contextual information. We introduce a sequence-attention module and distance-weighted similarity loss to aggregate contextual information and argumentative information. Our experiments on five datasets from various domains demonstrate that our model achieves state-of-the-art performance.
• Score: 79.06082391992545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents. Previous research has demonstrated that contextual information is crucial for developing an effective ASE model. However, we observe that merely concatenating sentences in a contextual window does not fully utilize contextual information and can sometimes lead to excessive attention on less informative sentences. To tackle this challenge, we propose an Efficient Context-aware ASE model (ECASE) that fully exploits contextual information by enhancing modeling capacity and augmenting training data. Specifically, we introduce a sequence-attention module and distance-weighted similarity loss to aggregate contextual information and argumentative information. Additionally, we augment the training data by randomly masking discourse markers and sentences, which reduces the model's reliance on specific words or less informative sentences. Our experiments on five datasets from various domains demonstrate that our model achieves state-of-the-art performance. Furthermore, ablation studies confirm the effectiveness of each module in our model.

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