@inproceedings{liu-etal-2019-discourse-representation,
title = "Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model",
author = "Liu, Jiangming and
Cohen, Shay B. and
Lapata, Mirella",
editor = "Abzianidze, Lasha and
van Noord, Rik and
Haagsma, Hessel and
Bos, Johan",
booktitle = "Proceedings of the {IWCS} Shared Task on Semantic Parsing",
month = may,
year = "2019",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1203",
doi = "10.18653/v1/W19-1203",
abstract = "We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8{\%} F1 in the DRS parsing shared task.",
}
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<abstract>We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8% F1 in the DRS parsing shared task.</abstract>
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%0 Conference Proceedings
%T Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model
%A Liu, Jiangming
%A Cohen, Shay B.
%A Lapata, Mirella
%Y Abzianidze, Lasha
%Y van Noord, Rik
%Y Haagsma, Hessel
%Y Bos, Johan
%S Proceedings of the IWCS Shared Task on Semantic Parsing
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F liu-etal-2019-discourse-representation
%X We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8% F1 in the DRS parsing shared task.
%R 10.18653/v1/W19-1203
%U https://aclanthology.org/W19-1203
%U https://doi.org/10.18653/v1/W19-1203
Markdown (Informal)
[Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model](https://aclanthology.org/W19-1203) (Liu et al., IWCS 2019)
ACL