Bridging Resolution: Making Sense of the State of the Art
Hideo Kobayashi, Vincent Ng · 2021
While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rulebased approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?)and ( 2) what should be improved.To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.