DiscSense: Automated Semantic Analysis of Discourse Markers

Damien Sileo, Tim Van de Cruys, Camille Pradel, Philippe Muller


Abstract
Using a model trained to predict discourse markers between sentence pairs, we predict plausible markers between sentence pairs with a known semantic relation (provided by existing classification datasets). These predictions allow us to study the link between discourse markers and the semantic relations annotated in classification datasets. Handcrafted mappings have been proposed between markers and discourse relations on a limited set of markers and a limited set of categories, but there exists hundreds of discourse markers expressing a wide variety of relations, and there is no consensus on the taxonomy of relations between competing discourse theories (which are largely built in a top-down fashion). By using an automatic prediction method over existing semantically annotated datasets, we provide a bottom-up characterization of discourse markers in English. The resulting dataset, named DiscSense, is publicly available.
Anthology ID:
2020.lrec-1.125
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
991–999
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.125
DOI:
Bibkey:
Cite (ACL):
Damien Sileo, Tim Van de Cruys, Camille Pradel, and Philippe Muller. 2020. DiscSense: Automated Semantic Analysis of Discourse Markers. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 991–999, Marseille, France. European Language Resources Association.
Cite (Informal):
DiscSense: Automated Semantic Analysis of Discourse Markers (Sileo et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.125.pdf
Code
 synapse-developpement/DiscSense
Data
Discovery DatasetGLUE