German and French Neural Supertagging Experiments for LTAG Parsing
Tatiana Bladier, Andreas van Cranenburgh, Younes Samih, Laura Kallmeyer
Abstract
We present ongoing work on data-driven parsing of German and French with Lexicalized Tree Adjoining Grammars. We use a supertagging approach combined with deep learning. We show the challenges of extracting LTAG supertags from the French Treebank, introduce the use of left- and right-sister-adjunction, present a neural architecture for the supertagger, and report experiments of n-best supertagging for French and German.- Anthology ID:
- P18-3009
- Volume:
- Proceedings of ACL 2018, Student Research Workshop
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Vered Shwartz, Jeniya Tabassum, Rob Voigt, Wanxiang Che, Marie-Catherine de Marneffe, Malvina Nissim
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 59–66
- Language:
- URL:
- https://aclanthology.org/P18-3009
- DOI:
- 10.18653/v1/P18-3009
- Cite (ACL):
- Tatiana Bladier, Andreas van Cranenburgh, Younes Samih, and Laura Kallmeyer. 2018. German and French Neural Supertagging Experiments for LTAG Parsing. In Proceedings of ACL 2018, Student Research Workshop, pages 59–66, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- German and French Neural Supertagging Experiments for LTAG Parsing (Bladier et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/P18-3009.pdf