Is Partial Linguistic Information Sufficient for Discourse Connective Disambiguation? A Case Study of Concession

Discourse relations are sometimes explicitly conveyed by specific connectives.However, some connectives can signal multiple discourse relations; in such cases, disambiguation is necessary to determine which relation is intended.This task is known as discourse connective disambiguation (Pitler and Nenkova, 2009), and particular attention is often given to connectives that can convey both CONCES-SION and other relations (e.g., SYNCHRONOUS).In this study, we conducted experiments to analyze which linguistic features play an important role in the disambiguation of polysemous connectives in Japanese.A neural language model (BERT) was fine-tuned using inputs from which specific linguistic features (e.g., word order, specific lexicon, etc.) had been removed.We analyzed which linguistic features affect disambiguation by comparing the model's performance.Our results show that even after performing drastic removal, such as deleting one of the two arguments that constitute the discourse relation, the model's performance remained relatively robust.However, the removal of certain lexical items or words belonging to specific lexical categories significantly degraded disambiguation performance, highlighting their importance in identifying the intended discourse relation.