@InProceedings{gruzitis-gosko-barzdins:2017:SemEval,
  author    = {Gruzitis, Normunds  and  Gosko, Didzis  and  Barzdins, Guntis},
  title     = {RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation},
  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     = {924--928},
  abstract  = {By addressing both text-to-AMR parsing and AMR-to-text generation, SemEval-2017
	Task 9 established AMR as a powerful semantic interlingua. We strengthen the
	interlingual aspect of AMR by applying the multilingual Grammatical Framework
	(GF) for AMR-to-text generation. Our current rule-based GF approach completely
	covered only 12.3% of the test AMRs, therefore we combined it with
	state-of-the-art JAMR Generator to see if the combination increases or
	decreases the overall performance. The combined system achieved the automatic
	BLEU score of 18.82 and the human Trueskill score of 107.2, to be compared to
	the plain JAMR Generator results. As for AMR parsing, we added NER extensions
	to our SemEval-2016 general-domain AMR parser to handle the biomedical genre,
	rich in organic compound names, achieving Smatch F1=54.0%.},
  url       = {http://www.aclweb.org/anthology/S17-2159}
}