Work

Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations

Year: 2022

Type: conference-paper

Abstract: Implicit discourse relation recognition is a challenging task that involves identifying the sense or senses that hold between two adjacent spans of text, in the absense of an explicit connective between them.In both PDTB-2 (Prasad et al., 2008) and PDTB-3 (Webber et al., 2019), discourse relational senses are organized into a three-level hierarchy ranging from four broad top-level senses, to more specific senses below them.Most previous work on implicitf discourse relation recognition have used the sense hierarchy simply to indicate what sense labels were available.Here we do more -incorporating the sense hierarchy into the recognition process itself and using it to select the negative examples used in contrastive learning.With no additional effort, the approach achieves stateof-the-art performance on the task.Our code is released in https://github.com/wanqiulong0923/Contrastive_IDRR.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/2022.emnlp-main.734

Authors: Wanqiu Long, Bonnie Webber

Institution: University of Edinburgh

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: UK Research and Innovation, University of Edinburgh

Award: G373773752