Wan Ju Kang


2024

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XFACT Team0331 at PerspectiveArg2024: Sampling from Bounded Clusters for Diverse Relevant Argument Retrieval
Wan Ju Kang | Jiyoung Han | Jaemin Jung | James Thorne
Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)

This paper reports on the argument mining system submitted to the ArgMining workshop 2024 for The Perspective Argument Retrieval Shared Task (Falk et al., 2024). We com- bine the strengths of a smaller Sentence BERT model and a Large Language Model: the for- mer is fine-tuned for a contrastive embedding objective and a classification objective whereas the latter is invoked to augment the query and populate the latent space with diverse relevant arguments. We conduct an ablation study on these components to find that each contributes substantially to the diversity and relevance cri- teria for the top-k retrieval of arguments from the given corpus.