@InProceedings{lampouras-vlachos:2017:SemEval,
  author    = {Lampouras, Gerasimos  and  Vlachos, Andreas},
  title     = {Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {586--591},
  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.},
  url       = {http://www.aclweb.org/anthology/S17-2096}
}