The rapid growth of online discussions has led to a wealth of user-generated text, rich in arguments and diverse in nature. However, the complexity of these informal arguments presents a challenge for argument mining. Argument mining is the task of automatically analysing arguments, such that the unstructured information contained in them is converted into structured representations. Current practice in argument mining largely focuses on well-structured and edited formal text, but the annotation schemes and models developed for these simpler texts cannot account well for the phenomena found in informal text.

To capture the characteristics of informal arguments, I designed an annotation scheme which includes undercuts, a counterargument device that challenges the relationship between a premise and a claim. Other computational approaches conflate undercuts with direct attacks, a device where the truth of the claim or the premise itself is challenged. I also presented the resultant large-scale Quora dataset featuring informal arguments, complemented by a layer of annotation detailing complete argument structures.

I then proposed an end-to-end approach to argument mining based on dependency parsing. My approach uses new dependency representations for arguments and two new neural dependency parsers, one based on biaffine parsing and the other on GNNs. It comfortably beats a strong baseline on the Quora dataset. When applied to an existing benchmark dataset of formal arguments, my approach establishes a new state of the art. It is also the first automatic argument mining approach that is able to recognise undercuts.

Furthermore, I conducted a study on external knowledge integration for end-to-end argument mining, such as information from syntax, discourse, knowledge graphs, and large language models. I found that feature-based integration using GPT-3.5 is the most effective method among those I have surveyed.

Overall, I hope that my work, by providing automatic analyses of arguments in online discussions, will eventually foster better understanding among people with different opinions.