Implicit Sense-labeled Connective Recognition as Text Generation

Yui Oka, Tsutomu Hirao

Submission Type: Regular Short Paper

Submission Track: Discourse and Pragmatics

Keywords: Implicit Discourse Relation Recognition, Implicit Sense-labeled Connective Recognition, Encoder-Decoder, PDTB-3.0

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, some downstream tasks require more than just a sense label as well as the specific connective used.
This paper presents Implicit Sense-labeled Connective Recognition (ISCR), which identifies the implicit connectives and their sense labels between adjacent text spans.
ISCR can be treated as a classification task, but a large number of potential categories, sense labels, and uneven distribution of instances among them make this difficult.
Instead, this paper handles the task as a text-generation task, using an encoder-decoder model to generate both connectives and their sense labels.
Here, we explore a classification method and three kinds of text-generation methods.
From our evaluation results on PDTB-3.0, we found that our method outperforms the conventional classification-based method.

Submission Number: 2542