Implicit Sense-labeled Connective Recognition

Abstract

Implicit Discourse Relation Recognition (IDRR) involves identifying the sense label of an implicit connective between adjacent text spans. This has traditionally been approached as a classification task. However, sense labels cannot exhaustively represent all discourse. This paper presents Implicit Sense-labeled Connective Recognition (ISCR), which identifies the implicit connectives as well as their sense labels between adjacent text spans. ISCR can be treated as a classification task, but it’s actually difficult due to the large number of potential categories, the use of sense labels, and the uneven distribution of instances among them. Accordingly, this paper instead handles ISCR as a text-generation task, using an encoder-decoder model to generate both connectives and their sense labels. From our evaluation results, we found that our classification method outperforms the conventional classification-based method.