Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs

Johanna Björklund, Shay B. Cohen, Frank Drewes, Giorgio Satta


Abstract
We propose a formal model for translating unranked syntactic trees, such as dependency trees, into semantic graphs. These tree-to-graph transducers can serve as a formal basis of transition systems for semantic parsing which recently have been shown to perform very well, yet hitherto lack formalization. Our model features “extended” rules and an arc-factored normal form, comes with an efficient translation algorithm, and can be equipped with weights in a straightforward manner.
Anthology ID:
W19-3104
Volume:
Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing
Month:
September
Year:
2019
Address:
Dresden, Germany
Editors:
Heiko Vogler, Andreas Maletti
Venue:
FSMNLP
SIG:
SIGFSM
Publisher:
Association for Computational Linguistics
Note:
Pages:
7–17
Language:
URL:
https://aclanthology.org/W19-3104
DOI:
10.18653/v1/W19-3104
Bibkey:
Cite (ACL):
Johanna Björklund, Shay B. Cohen, Frank Drewes, and Giorgio Satta. 2019. Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs. In Proceedings of the 14th International Conference on Finite-State Methods and Natural Language Processing, pages 7–17, Dresden, Germany. Association for Computational Linguistics.
Cite (Informal):
Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs (Björklund et al., FSMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-3104.pdf