Stance-aware Definition Generation for Argumentative Texts

Definition generation models trained on dictionary data are generally expected to produce neutral and unbiased output while capturing the contextual nuances.However, previous studies have shown that generated definitions can inherit biases from both the underlying models and the input context.This paper examines the extent to which stance-related bias in argumentative data influences the generated definitions.In particular, we train a model on a slang-based dictionary to explore the feasibility of generating persuasive definitions that concisely reflect opposing parties' understandings of contested terms.Through this study, we provide new insights into bias propagation in definition generation and its implications for definition generation applications and argument mining.