DRS Parsing as Sequence Labeling

Minxing Shen, Kilian Evang


Abstract
We present the first fully trainable semantic parser for English, German, Italian, and Dutch discourse representation structures (DRSs) that is competitive in accuracy with recent sequence-to-sequence models and at the same time {emph{compositional} in the sense that the output maps each token to one of a finite set of meaning {emph{fragments}, and the meaning of the utterance is a function of the meanings of its parts. We argue that this property makes the system more transparent and more useful for human-in-the-loop annotation. We achieve this simply by casting DRS parsing as a sequence labeling task, where tokens are labeled with both fragments (lists of abstracted clauses with relative referent indices indicating unification) and {emph{symbols} like word senses or names. We give a comprehensive error analysis that highlights areas for future work.
Anthology ID:
2022.starsem-1.19
Volume:
Proceedings of the 11th Joint Conference on Lexical and Computational Semantics
Month:
July
Year:
2022
Address:
Seattle, Washington
Venues:
*SEM | NAACL
SIG:
SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
213–225
Language:
URL:
https://aclanthology.org/2022.starsem-1.19
DOI:
10.18653/v1/2022.starsem-1.19
Bibkey:
Cite (ACL):
Minxing Shen and Kilian Evang. 2022. DRS Parsing as Sequence Labeling. In Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, pages 213–225, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
DRS Parsing as Sequence Labeling (Shen & Evang, *SEM 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.starsem-1.19.pdf