Which aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification

Charlotte Roze, Chloé Braud, Philippe Muller


Abstract
Discourse relation classification has proven to be a hard task, with rather low performance on several corpora that notably differ on the relation set they use. We propose to decompose the task into smaller, mostly binary tasks corresponding to various primitive concepts encoded into the discourse relation definitions. More precisely, we translate the discourse relations into a set of values for attributes based on distinctions used in the mappings between discourse frameworks proposed by Sanders et al. (2018). This arguably allows for a more robust representation of discourse relations, and enables us to address usually ignored aspects of discourse relation prediction, namely multiple labels and underspecified annotations. We show experimentally which of the conceptual primitives are harder to learn from the Penn Discourse Treebank English corpus, and propose a correspondence to predict the original labels, with preliminary empirical comparisons with a direct model.
Anthology ID:
W19-5950
Volume:
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
Month:
September
Year:
2019
Address:
Stockholm, Sweden
Editors:
Satoshi Nakamura, Milica Gasic, Ingrid Zukerman, Gabriel Skantze, Mikio Nakano, Alexandros Papangelis, Stefan Ultes, Koichiro Yoshino
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
432–441
Language:
URL:
https://aclanthology.org/W19-5950
DOI:
10.18653/v1/W19-5950
Bibkey:
Cite (ACL):
Charlotte Roze, Chloé Braud, and Philippe Muller. 2019. Which aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 432–441, Stockholm, Sweden. Association for Computational Linguistics.
Cite (Informal):
Which aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification (Roze et al., SIGDIAL 2019)
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PDF:
https://preview.aclanthology.org/fix-dup-bibkey/W19-5950.pdf