@inproceedings{ozaki-etal-2020-hitachi,
title = "Hitachi at {MRP} 2020: Text-to-Graph-Notation Transducer",
author = "Ozaki, Hiroaki and
Morio, Gaku and
Koreeda, Yuta and
Morishita, Terufumi and
Miyoshi, Toshinori",
editor = "Oepen, Stephan and
Abend, Omri and
Abzianidze, Lasha and
Bos, Johan and
Haji{\v{c}}, Jan and
Hershcovich, Daniel and
Li, Bin and
O'Gorman, Tim and
Xue, Nianwen and
Zeman, Daniel",
booktitle = "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.conll-shared.4",
doi = "10.18653/v1/2020.conll-shared.4",
pages = "40--52",
abstract = "This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages. We propose to unify these tasks as a text-to-graph-notation transduction in which we convert an input text into a graph notation. To this end, we designed a novel Plain Graph Notation (PGN) that handles various graphs universally. Then, our parser predicts a PGN-based sequence by leveraging Transformers and biaffine attentions. Notably, our parser can handle any PGN-formatted graphs with fewer framework-specific modifications. As a result, ensemble versions of the parser tied for 1st place in both cross-framework and cross-lingual tracks.",
}
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%0 Conference Proceedings
%T Hitachi at MRP 2020: Text-to-Graph-Notation Transducer
%A Ozaki, Hiroaki
%A Morio, Gaku
%A Koreeda, Yuta
%A Morishita, Terufumi
%A Miyoshi, Toshinori
%Y Oepen, Stephan
%Y Abend, Omri
%Y Abzianidze, Lasha
%Y Bos, Johan
%Y Hajič, Jan
%Y Hershcovich, Daniel
%Y Li, Bin
%Y O’Gorman, Tim
%Y Xue, Nianwen
%Y Zeman, Daniel
%S Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ozaki-etal-2020-hitachi
%X This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages. We propose to unify these tasks as a text-to-graph-notation transduction in which we convert an input text into a graph notation. To this end, we designed a novel Plain Graph Notation (PGN) that handles various graphs universally. Then, our parser predicts a PGN-based sequence by leveraging Transformers and biaffine attentions. Notably, our parser can handle any PGN-formatted graphs with fewer framework-specific modifications. As a result, ensemble versions of the parser tied for 1st place in both cross-framework and cross-lingual tracks.
%R 10.18653/v1/2020.conll-shared.4
%U https://aclanthology.org/2020.conll-shared.4
%U https://doi.org/10.18653/v1/2020.conll-shared.4
%P 40-52
Markdown (Informal)
[Hitachi at MRP 2020: Text-to-Graph-Notation Transducer](https://aclanthology.org/2020.conll-shared.4) (Ozaki et al., CoNLL 2020)
ACL
- Hiroaki Ozaki, Gaku Morio, Yuta Koreeda, Terufumi Morishita, and Toshinori Miyoshi. 2020. Hitachi at MRP 2020: Text-to-Graph-Notation Transducer. In Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing, pages 40–52, Online. Association for Computational Linguistics.