From OpenCCG to AI Planning: Detecting Infeasible Edges in Sentence Generation

Maximilian Schwenger, Álvaro Torralba, Joerg Hoffmann, David M. Howcroft, Vera Demberg

Abstract
The search space in grammar-based natural language generation tasks can get very large, which is particularly problematic when generating long utterances or paragraphs. Using surface realization with OpenCCG as an example, we show that we can effectively detect partial solutions (edges) which cannot ultimately be part of a complete sentence because of their syntactic category. Formulating the completion of an edge into a sentence as finding a solution path in a large state-transition system, we demonstrate a connection to AI Planning which is concerned with this kind of problem. We design a compilation from OpenCCG into AI Planning allowing the detection of infeasible edges via AI Planning dead-end detection methods (proving the absence of a solution to the compilation). Our experiments show that this can filter out large fractions of infeasible edges in, and thus benefit the performance of, complex realization processes.
Anthology ID:
C16-1144
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1524–1534
Language:
URL:
https://aclanthology.org/C16-1144
DOI:
Bibkey:
Cite (ACL):
Maximilian Schwenger, Álvaro Torralba, Joerg Hoffmann, David M. Howcroft, and Vera Demberg. 2016. From OpenCCG to AI Planning: Detecting Infeasible Edges in Sentence Generation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1524–1534, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
From OpenCCG to AI Planning: Detecting Infeasible Edges in Sentence Generation (Schwenger et al., COLING 2016)
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