Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.

Gerasimos Lampouras, Andreas Vlachos


Abstract
This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2). We cast language generation from AMR as a sequence of actions (e.g., insert/remove/rename edges and nodes) that progressively transform the AMR graph into a dependency parse tree. This transition-based approach relies on the fact that an AMR graph can be considered structurally similar to a dependency tree, with a focus on content rather than function words. An added benefit to this approach is the greater amount of data we can take advantage of to train the parse-to-text linearizer. Our submitted run on the test data achieved a BLEU score of 3.32 and a Trueskill score of -22.04 on automatic and human evaluation respectively.
Anthology ID:
S17-2096
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
586–591
Language:
URL:
https://www.aclweb.org/anthology/S17-2096
DOI:
10.18653/v1/S17-2096
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2096.pdf