Argument Mining Task

Argument mining is the computational task of automatically identifying and classifying components of arguments within text, aiming to understand the structure and persuasiveness of discourse. Current research focuses on improving model performance through techniques like multi-task learning, leveraging audio features from speech data, and employing transfer learning from pre-trained language models, often utilizing architectures such as BERT-based cascade models. This field is significant for its applications in various domains, including political analysis, market research, and the development of AI debaters, by enabling automated analysis of large-scale textual and spoken argumentation. The development of larger, more comprehensive datasets is also a key area of ongoing work.