Exploring sentence embeddings for better argument relation classification

Abstract

This thesis investigates methods to improve argument relation classification by augmenting sentence embeddings and leveraging Large Language Models (LLMs). Argument relation classification, a subtask of argument mining, involves predicting relationships, such as support or attack, between argumentative components in unstructured text. While sentence embeddings have demonstrated state-of-the-art performance in standard text classification, little research has examined their potential for argument relation classification. To address this gap, we evaluate three sentence embedding models—InferSent, Sent2Vec, and SBERT—on text classification tasks. Our findings show that SBERT performs best, underscoring the importance of Transformer-based architectures in capturing sentence semantics. However, as SBERT relies on annotated data, we explore self-supervised fine-tuning of BERT with contrastive learning and various data augmentation strategies to create domain-adaptive sentence embeddings. These embeddings outperform SBERT on text classification tasks when pre-trained and fine-tuned specifically for the domain. Building on this approach, we apply domainadaptive BERT to argument relation classification, achieving superior results over baseline models. Additionally, we investigate the use of LLMs for argument relation classification, combining sentence embeddings with LLMs using direct and Chain-of-Thought prompting strategies. Fine-tuned Llama2 models with Chain-of-Thought prompting outperform baseline methods, demonstrating the value of LLMs in capturing complex argumentative structures across multi-sentence inputs. This work contributes to argument mining by advancing the use of sentence embeddings and LLMs in argument relation classification.

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Identifiers

ORCID for Jiatong Zhu: orcid.org/0009-0003-9060-9003

ORCID for Stuart Middleton: orcid.org/0000-0001-8305-8176

ORCID for Tim Norman: orcid.org/0000-0002-6387-4034

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