@inproceedings{cao-clark-2019-factorising,
title = "Factorising {AMR} generation through syntax",
author = "Cao, Kris and
Clark, Stephen",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1223",
doi = "10.18653/v1/N19-1223",
pages = "2157--2163",
abstract = "Generating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not specified by the semantic graph. To explicitly account for this variation, we break down generating from AMR into two steps: first generate a syntactic structure, and then generate the surface form. We show that decomposing the generation process this way leads to state-of-the-art single model performance generating from AMR without additional unlabelled data. We also demonstrate that we can generate meaning-preserving syntactic paraphrases of the same AMR graph, as judged by humans.",
}
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%0 Conference Proceedings
%T Factorising AMR generation through syntax
%A Cao, Kris
%A Clark, Stephen
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F cao-clark-2019-factorising
%X Generating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not specified by the semantic graph. To explicitly account for this variation, we break down generating from AMR into two steps: first generate a syntactic structure, and then generate the surface form. We show that decomposing the generation process this way leads to state-of-the-art single model performance generating from AMR without additional unlabelled data. We also demonstrate that we can generate meaning-preserving syntactic paraphrases of the same AMR graph, as judged by humans.
%R 10.18653/v1/N19-1223
%U https://aclanthology.org/N19-1223
%U https://doi.org/10.18653/v1/N19-1223
%P 2157-2163
Markdown (Informal)
[Factorising AMR generation through syntax](https://aclanthology.org/N19-1223) (Cao & Clark, NAACL 2019)
ACL
- Kris Cao and Stephen Clark. 2019. Factorising AMR generation through syntax. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2157–2163, Minneapolis, Minnesota. Association for Computational Linguistics.