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 Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 991–999
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.125
- DOI:
- 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 Twelfth 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.125.pdf
- Code
- synapse-developpement/DiscSense
- Data
- Discovery, GLUE