@inproceedings{zhang-etal-2019-suda,
title = "{SUDA}-{A}libaba at {MRP} 2019: Graph-Based Models with {BERT}",
author = "Zhang, Yue and
Jiang, Wei and
Xia, Qingrong and
Cao, Junjie and
Wang, Rui and
Li, Zhenghua and
Zhang, Min",
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/K19-2014",
doi = "10.18653/v1/K19-2014",
pages = "149--157",
abstract = "In this paper, we describe our participating systems in the shared task on Cross- Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). The task includes five frameworks for graph-based meaning representations, i.e., DM, PSD, EDS, UCCA, and AMR. One common characteristic of our systems is that we employ graph-based methods instead of transition-based methods when predicting edges between nodes. For SDP, we jointly perform edge prediction, frame tagging, and POS tagging via multi-task learning (MTL). For UCCA, we also jointly model a constituent tree parsing and a remote edge recovery task. For both EDS and AMR, we produce nodes first and edges second in a pipeline fashion. External resources like BERT are found helpful for all frameworks except AMR. Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.",
}
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<abstract>In this paper, we describe our participating systems in the shared task on Cross- Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). The task includes five frameworks for graph-based meaning representations, i.e., DM, PSD, EDS, UCCA, and AMR. One common characteristic of our systems is that we employ graph-based methods instead of transition-based methods when predicting edges between nodes. For SDP, we jointly perform edge prediction, frame tagging, and POS tagging via multi-task learning (MTL). For UCCA, we also jointly model a constituent tree parsing and a remote edge recovery task. For both EDS and AMR, we produce nodes first and edges second in a pipeline fashion. External resources like BERT are found helpful for all frameworks except AMR. Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.</abstract>
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%0 Conference Proceedings
%T SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT
%A Zhang, Yue
%A Jiang, Wei
%A Xia, Qingrong
%A Cao, Junjie
%A Wang, Rui
%A Li, Zhenghua
%A Zhang, Min
%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 zhang-etal-2019-suda
%X In this paper, we describe our participating systems in the shared task on Cross- Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). The task includes five frameworks for graph-based meaning representations, i.e., DM, PSD, EDS, UCCA, and AMR. One common characteristic of our systems is that we employ graph-based methods instead of transition-based methods when predicting edges between nodes. For SDP, we jointly perform edge prediction, frame tagging, and POS tagging via multi-task learning (MTL). For UCCA, we also jointly model a constituent tree parsing and a remote edge recovery task. For both EDS and AMR, we produce nodes first and edges second in a pipeline fashion. External resources like BERT are found helpful for all frameworks except AMR. Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.
%R 10.18653/v1/K19-2014
%U https://aclanthology.org/K19-2014
%U https://doi.org/10.18653/v1/K19-2014
%P 149-157
Markdown (Informal)
[SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT](https://aclanthology.org/K19-2014) (Zhang et al., CoNLL 2019)
ACL
- Yue Zhang, Wei Jiang, Qingrong Xia, Junjie Cao, Rui Wang, Zhenghua Li, and Min Zhang. 2019. SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT. In Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning, pages 149–157, Hong Kong. Association for Computational Linguistics.