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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P18-3009.pdf