在线对话中的论证结构预测：建模范式与任务架构的比较研究
Argument Structure Prediction in Online Conversations: A Comparative Study of Modeling Paradigms and Task Architectures
- Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
- Universidade da Coruña（拉科鲁尼亚大学）
- IEGPS-CSIC, Spanish National Research Council（西班牙国家研究委员会IEGPS-CSIC）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究系统比较了监督微调与基于提示的LLM在对话论证结构预测中的单步与多步架构，发现识别论证关系是主要瓶颈，并发布了数据处理与建模框架。
中文摘要 AI 辅助
论证结构预测（ASP）通过识别论证单元及其关系，从话语中构建完整的论证结构。尽管近期工作探索了多种方法——包括统一神经模型、多步骤流水线和基于提示的大语言模型（LLM）——但它们之间的相对权衡仍未得到充分研究，尤其是在对话场景中。我们在严格的模式约束下对ASP进行了系统评估，比较了监督微调和基于提示的LLM在单步和多步任务架构中的表现，从对话输入端到端地生成完整的论证结构。我们在三个不同的对话语料库上进行了基准测试，这些语料库改编自推理锚定理论，形成双极论证结构。在共享评估框架下，我们评估了预测性能、跨领域泛化能力、模式合规性和计算效率。我们的结果表明，ASP仍然是一项具有挑战性的任务，识别论证关系成为主要瓶颈，这主要是由于对话论证的隐性和上下文依赖性。为促进未来研究，我们发布了用于对话语料库计算ASP的数据处理流水线和端到端建模框架。
英文摘要
Argument structure prediction (ASP) constructs complete argument structures from discourse by identifying argumentative units and their relations. While recent work has explored diverse approaches---including unified neural models, multi-step pipelines, and prompt-based large language models (LLMs)---their relative trade-offs remain under-explored, particularly in dialogical settings. We present a systematic evaluation of ASP under strict schema constraints, comparing supervised fine-tuning and prompt-based LLMs across single- and multi-step task architectures, generating complete argument structures from dialogical input end-to-end. We benchmark them on three diverse dialogical corpora adapted from Inference Anchoring Theory into bipolar argument structures. Under a shared evaluation framework, we assess predictive performance, cross-domain generalization, schema compliance, and computational efficiency. Our results show that ASP remains a challenging task, with identifying argumentative relations emerging as the primary bottleneck, largely due to the implicit and context-dependent nature of dialogical argumentation. To facilitate future research, we release our data processing pipeline and end-to-end modeling framework for computational ASP on dialogical corpora.
