Recurrent models and lower bounds for projective syntactic decoding
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review
Open access
Publication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOriginal language
EnglishPages from-to (Number of pages)
Pages 251-260 (10 pages)Publication milestones
- Published - 2019
Publication status
Published - 2019
Volume
Volume 1 (Long and Short Papers)Publisher
Association for Computational Linguistics, United StatesPublication IDs
- Scopus: 85085556018
Host publication title
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language TechnologiesAbstract
The current state-of-the-art in neural graphbased parsing uses only approximate decoding at the training phase. In this paper aim to understand this result better. We show how recurrent models can carry out projective maximum spanning tree decoding. This result holds for both current state-of-the-art models for shiftreduce
and graph-based parsers, projective or not. We also provide the first proof on the
lower bounds of projective maximum spanning tree, DAG, and digraph decoding.
and graph-based parsers, projective or not. We also provide the first proof on the
lower bounds of projective maximum spanning tree, DAG, and digraph decoding.
Publication metrics
PlumX, opens in new tab
Captures
79