@inproceedings{szubert-etal-2020-role,
title = "The Role of Reentrancies in {A}bstract {M}eaning {R}epresentation Parsing",
author = "Szubert, Ida and
Damonte, Marco and
Cohen, Shay B. and
Steedman, Mark",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.199",
doi = "10.18653/v1/2020.findings-emnlp.199",
pages = "2198--2207",
abstract = "Abstract Meaning Representation (AMR) parsing aims at converting sentences into AMR representations. These are graphs and not trees because AMR supports reentrancies (nodes with more than one parent). Following previous findings on the importance of reen- trancies for AMR, we empirically find and discuss several linguistic phenomena respon- sible for reentrancies in AMR, some of which have not received attention before. We cate- gorize the types of errors AMR parsers make with respect to reentrancies. Furthermore, we find that correcting these errors provides an in- crease of up to 5{\%} Smatch in parsing perfor- mance and 20{\%} in reentrancy prediction",
}
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<abstract>Abstract Meaning Representation (AMR) parsing aims at converting sentences into AMR representations. These are graphs and not trees because AMR supports reentrancies (nodes with more than one parent). Following previous findings on the importance of reen- trancies for AMR, we empirically find and discuss several linguistic phenomena respon- sible for reentrancies in AMR, some of which have not received attention before. We cate- gorize the types of errors AMR parsers make with respect to reentrancies. Furthermore, we find that correcting these errors provides an in- crease of up to 5% Smatch in parsing perfor- mance and 20% in reentrancy prediction</abstract>
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%0 Conference Proceedings
%T The Role of Reentrancies in Abstract Meaning Representation Parsing
%A Szubert, Ida
%A Damonte, Marco
%A Cohen, Shay B.
%A Steedman, Mark
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F szubert-etal-2020-role
%X Abstract Meaning Representation (AMR) parsing aims at converting sentences into AMR representations. These are graphs and not trees because AMR supports reentrancies (nodes with more than one parent). Following previous findings on the importance of reen- trancies for AMR, we empirically find and discuss several linguistic phenomena respon- sible for reentrancies in AMR, some of which have not received attention before. We cate- gorize the types of errors AMR parsers make with respect to reentrancies. Furthermore, we find that correcting these errors provides an in- crease of up to 5% Smatch in parsing perfor- mance and 20% in reentrancy prediction
%R 10.18653/v1/2020.findings-emnlp.199
%U https://aclanthology.org/2020.findings-emnlp.199
%U https://doi.org/10.18653/v1/2020.findings-emnlp.199
%P 2198-2207
Markdown (Informal)
[The Role of Reentrancies in Abstract Meaning Representation Parsing](https://aclanthology.org/2020.findings-emnlp.199) (Szubert et al., Findings 2020)
ACL