@inproceedings{wang-xue-2017-getting,
title = "Getting the Most out of {AMR} Parsing",
author = "Wang, Chuan and
Xue, Nianwen",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1129",
doi = "10.18653/v1/D17-1129",
pages = "1257--1268",
abstract = "This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-based word-to-concept alignment model with graph distance distortion and a rescoring method during decoding to incorporate the structural information in the AMR graph. We show integrating the two components into an existing AMR parser results in consistently better performance over the state of the art on various datasets.",
}
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%0 Conference Proceedings
%T Getting the Most out of AMR Parsing
%A Wang, Chuan
%A Xue, Nianwen
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-xue-2017-getting
%X This paper proposes to tackle the AMR parsing bottleneck by improving two components of an AMR parser: concept identification and alignment. We first build a Bidirectional LSTM based concept identifier that is able to incorporate richer contextual information to learn sparse AMR concept labels. We then extend an HMM-based word-to-concept alignment model with graph distance distortion and a rescoring method during decoding to incorporate the structural information in the AMR graph. We show integrating the two components into an existing AMR parser results in consistently better performance over the state of the art on various datasets.
%R 10.18653/v1/D17-1129
%U https://aclanthology.org/D17-1129
%U https://doi.org/10.18653/v1/D17-1129
%P 1257-1268
Markdown (Informal)
[Getting the Most out of AMR Parsing](https://aclanthology.org/D17-1129) (Wang & Xue, EMNLP 2017)
ACL
- Chuan Wang and Nianwen Xue. 2017. Getting the Most out of AMR Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1257–1268, Copenhagen, Denmark. Association for Computational Linguistics.