@inproceedings{van-noord-bos-2017-meaning,
title = "The Meaning Factory at {S}em{E}val-2017 Task 9: Producing {AMR}s with Neural Semantic Parsing",
author = "van Noord, Rik and
Bos, Johan",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2160",
doi = "10.18653/v1/S17-2160",
pages = "929--933",
abstract = "We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs. With data augmentation, super characters, and POS-tagging we gain major improvements in performance compared to a baseline character-level model. Although we improve on previous character-based neural semantic parsing models, the overall accuracy is still lower than a state-of-the-art AMR parser. An ensemble combining our neural semantic parser with an existing, traditional parser, yields a small gain in performance.",
}
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%0 Conference Proceedings
%T The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
%A van Noord, Rik
%A Bos, Johan
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F van-noord-bos-2017-meaning
%X We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs. With data augmentation, super characters, and POS-tagging we gain major improvements in performance compared to a baseline character-level model. Although we improve on previous character-based neural semantic parsing models, the overall accuracy is still lower than a state-of-the-art AMR parser. An ensemble combining our neural semantic parser with an existing, traditional parser, yields a small gain in performance.
%R 10.18653/v1/S17-2160
%U https://aclanthology.org/S17-2160
%U https://doi.org/10.18653/v1/S17-2160
%P 929-933
Markdown (Informal)
[The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing](https://aclanthology.org/S17-2160) (van Noord & Bos, SemEval 2017)
ACL