论证挖掘研究现状与进展

Research Advances in Argument Mining

[目的/意义]论证挖掘是近年来计算语言学领域的热点研究内容,为论证计算模型提供机器可处理的结构化数据,对其研究现状及进展进行总结和分析可为后续的研究及应用深化提供借鉴。[方法/过程]本研究通过对国内外论证挖掘重要文献进行收集、整理和分析,从相关研究基础、技术体系、应用实践等多个维度进行系统性综述,通过梳理总结论证挖掘发展路径展示该研究领域的发展全貌和特征,并重点描述多模态论证挖掘研究的现状。[结果/结论]论证挖掘任务与自然语言处理等人工智能技术息息相关,相关研究经历了“机器学习-深度学习”“文本-多模态”的发展变迁,且领域发展与应用水平不一;如何实现多粒度、多模态内容泛化,以及如何促进其应用落地实践将是下一步研究的热点和重点。

[Purpose/Significance]Argument mining,a research hotspot in the field of computational linguistics,provides machine processable structured data for computational models of argument.Argument mining tasks are closely related to artificial intelligence(AI)technologies,such as natural language processing and knowledge representation.There are numerous systematic studies in academia and a clear technical realization route has come into being.New research results continue to emerge as a result of rich resources and rapid development and iteration of deep learning,large language models(LLMs),and other technologies.This study,which reviews the research status and progress of argument mining,can serve as a resource for future research and application development.[Method/Process]Through literature review,this paper systematically reviews the relevant research basis(including foundational techniques and semantic representation models),summarizes the related technical system in terms of task framework,influencing factors of technological complexity,and method classification,and then introduces the argument mining practice and application cases for specific fields and research objectives and makes a comparative analysis.Most importantly,the overall development and characteristics of this research field are summarized,with a focus on tracking the progress of multimedia argument mining in the context of the new AI environment.[Results/Conclusions]Relevant research has experienced the development of"machine learning-deep learning"and"text only-multimodal",and the levels of development and application of various fields vary much.Future research may focus on how to achieve multigranularity and multimodal content generalization,as well as how to promote its application and implementation in practice.Possible research directions include:1)the use of LLMs in argument mining,because they exhibit significant benefits in downstream applications such as natural language processing and multimodal learning,and can also provide certain technical conditions for the generation of argument content;2)the use of domain knowledge organization systems such as vocabulary,knowledge base and knowledge graph:with these systems,researchers can combine domain-specific argument mining models with rich knowledge structure,to strengthen semantic representation and organization improve the systematization and dig deeper into argument mining model research in the domain;3)promoting the application research and practice of argument mining in more fields or across disciplines,and improving the retrieval and visualization of argument information,such as combining information retrieval methods with argument mining to build the next generation of argument search engines.