@inproceedings{ballesteros-al-onaizan-2017-amr,
title = "{AMR} Parsing using Stack-{LSTM}s",
author = "Ballesteros, Miguel and
Al-Onaizan, Yaser",
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/D17-1130",
doi = "10.18653/v1/D17-1130",
pages = "1269--1275",
abstract = "We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further.",
}
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%0 Conference Proceedings
%T AMR Parsing using Stack-LSTMs
%A Ballesteros, Miguel
%A Al-Onaizan, Yaser
%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 ballesteros-al-onaizan-2017-amr
%X We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further.
%R 10.18653/v1/D17-1130
%U https://aclanthology.org/D17-1130
%U https://doi.org/10.18653/v1/D17-1130
%P 1269-1275
Markdown (Informal)
[AMR Parsing using Stack-LSTMs](https://aclanthology.org/D17-1130) (Ballesteros & Al-Onaizan, EMNLP 2017)
ACL
- Miguel Ballesteros and Yaser Al-Onaizan. 2017. AMR Parsing using Stack-LSTMs. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1269–1275, Copenhagen, Denmark. Association for Computational Linguistics.