@inproceedings{li-etal-2019-sjtu,
title = "{SJTU}-{NICT} at {MRP} 2019: Multi-Task Learning for End-to-End Uniform Semantic Graph Parsing",
author = "Li, Zuchao and
Zhao, Hai and
Zhang, Zhuosheng and
Wang, Rui and
Utiyama, Masao and
Sumita, Eiichiro",
editor = "Oepen, Stephan and
Abend, Omri and
Hajic, Jan and
Hershcovich, Daniel and
Kuhlmann, Marco and
O{'}Gorman, Tim and
Xue, Nianwen",
booktitle = "Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/K19-2004/",
doi = "10.18653/v1/K19-2004",
pages = "45--54",
abstract = "This paper describes our SJTU-NICT{'}s system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). Our system uses a graph-based approach to model a variety of semantic graph parsing tasks. Our main contributions in the submitted system are summarized as follows: 1. Our model is fully end-to-end and is capable of being trained only on the given training set which does not rely on any other extra training source including the companion data provided by the organizer; 2. We extend our graph pruning algorithm to a variety of semantic graphs, solving the problem of excessive semantic graph search space; 3. We introduce multi-task learning for multiple objectives within the same framework. The evaluation results show that our system achieved second place in the overall $F_1$ score and achieved the best $F_1$ score on the DM framework."
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<abstract>This paper describes our SJTU-NICT’s system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). Our system uses a graph-based approach to model a variety of semantic graph parsing tasks. Our main contributions in the submitted system are summarized as follows: 1. Our model is fully end-to-end and is capable of being trained only on the given training set which does not rely on any other extra training source including the companion data provided by the organizer; 2. We extend our graph pruning algorithm to a variety of semantic graphs, solving the problem of excessive semantic graph search space; 3. We introduce multi-task learning for multiple objectives within the same framework. The evaluation results show that our system achieved second place in the overall F₁ score and achieved the best F₁ score on the DM framework.</abstract>
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%0 Conference Proceedings
%T SJTU-NICT at MRP 2019: Multi-Task Learning for End-to-End Uniform Semantic Graph Parsing
%A Li, Zuchao
%A Zhao, Hai
%A Zhang, Zhuosheng
%A Wang, Rui
%A Utiyama, Masao
%A Sumita, Eiichiro
%Y Oepen, Stephan
%Y Abend, Omri
%Y Hajic, Jan
%Y Hershcovich, Daniel
%Y Kuhlmann, Marco
%Y O’Gorman, Tim
%Y Xue, Nianwen
%S Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F li-etal-2019-sjtu
%X This paper describes our SJTU-NICT’s system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). Our system uses a graph-based approach to model a variety of semantic graph parsing tasks. Our main contributions in the submitted system are summarized as follows: 1. Our model is fully end-to-end and is capable of being trained only on the given training set which does not rely on any other extra training source including the companion data provided by the organizer; 2. We extend our graph pruning algorithm to a variety of semantic graphs, solving the problem of excessive semantic graph search space; 3. We introduce multi-task learning for multiple objectives within the same framework. The evaluation results show that our system achieved second place in the overall F₁ score and achieved the best F₁ score on the DM framework.
%R 10.18653/v1/K19-2004
%U https://aclanthology.org/K19-2004/
%U https://doi.org/10.18653/v1/K19-2004
%P 45-54
Markdown (Informal)
[SJTU-NICT at MRP 2019: Multi-Task Learning for End-to-End Uniform Semantic Graph Parsing](https://aclanthology.org/K19-2004/) (Li et al., CoNLL 2019)
ACL