@inproceedings{buys-blunsom-2017-oxford,
title = "{O}xford at {S}em{E}val-2017 Task 9: Neural {AMR} Parsing with Pointer-Augmented Attention",
author = "Buys, Jan and
Blunsom, Phil",
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/S17-2157",
doi = "10.18653/v1/S17-2157",
pages = "914--919",
abstract = "We present a neural encoder-decoder AMR parser that extends an attention-based model by predicting the alignment between graph nodes and sentence tokens explicitly with a pointer mechanism. Candidate lemmas are predicted as a pre-processing step so that the lemmas of lexical concepts, as well as constant strings, are factored out of the graph linearization and recovered through the predicted alignments. The approach does not rely on syntactic parses or extensive external resources. Our parser obtained 59{\%} Smatch on the SemEval test set.",
}
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<abstract>We present a neural encoder-decoder AMR parser that extends an attention-based model by predicting the alignment between graph nodes and sentence tokens explicitly with a pointer mechanism. Candidate lemmas are predicted as a pre-processing step so that the lemmas of lexical concepts, as well as constant strings, are factored out of the graph linearization and recovered through the predicted alignments. The approach does not rely on syntactic parses or extensive external resources. Our parser obtained 59% Smatch on the SemEval test set.</abstract>
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%0 Conference Proceedings
%T Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention
%A Buys, Jan
%A Blunsom, Phil
%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 buys-blunsom-2017-oxford
%X We present a neural encoder-decoder AMR parser that extends an attention-based model by predicting the alignment between graph nodes and sentence tokens explicitly with a pointer mechanism. Candidate lemmas are predicted as a pre-processing step so that the lemmas of lexical concepts, as well as constant strings, are factored out of the graph linearization and recovered through the predicted alignments. The approach does not rely on syntactic parses or extensive external resources. Our parser obtained 59% Smatch on the SemEval test set.
%R 10.18653/v1/S17-2157
%U https://aclanthology.org/S17-2157
%U https://doi.org/10.18653/v1/S17-2157
%P 914-919
Markdown (Informal)
[Oxford at SemEval-2017 Task 9: Neural AMR Parsing with Pointer-Augmented Attention](https://aclanthology.org/S17-2157) (Buys & Blunsom, SemEval 2017)
ACL