Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model

Jiangming Liu; Shay B. Cohen; Mirella Lapata

Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model

Jiangming Liu, Shay B. Cohen, Mirella Lapata

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the IWCS Shared Task on Semantic Parsing
Place of Publication	Gothenburg, Sweden
Publisher	Association for Computational Linguistics
Number of pages	6
Publication status	Published - 23 May 2019

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Discourse Representation Structure_LIU_DoA080419_VoR_CC-BYFinal published version, 175 KBLicence: Creative Commons: Attribution (CC-BY)

https://www.aclweb.org/anthology/W19-1203Licence: Creative Commons: Attribution (CC-BY)

TransModal: Translating from Multiple Modalities into Text
Lapata, M. (Principal Investigator)
EU government bodies
1/09/16 → 31/08/22
Project: Research
SUMMA - Scalable Understanding of Mulitingual Media
Renals, S. (Principal Investigator), Birch-Mayne, A. (Co-investigator) & Cohen, S. (Co-investigator)
EU government bodies
1/02/16 → 31/01/19
Project: Research

Cite this

@inproceedings{82f53598d581429b8cbbd4ace88706e7,

title = "Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model",

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.",

author = "Jiangming Liu and Cohen, {Shay B.} and Mirella Lapata",

year = "2019",

month = may,

day = "23",

language = "English",

booktitle = "Proceedings of the IWCS Shared Task on Semantic Parsing",

publisher = "Association for Computational Linguistics",

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TY - GEN

T1 - Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model

AU - Liu, Jiangming

AU - Cohen, Shay B.

AU - Lapata, Mirella

PY - 2019/5/23

Y1 - 2019/5/23

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Proceedings of the IWCS Shared Task on Semantic Parsing

PB - Association for Computational Linguistics

CY - Gothenburg, Sweden

ER -

Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model

Abstract

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Projects

TransModal: Translating from Multiple Modalities into Text

SUMMA - Scalable Understanding of Mulitingual Media

Cite this