The Role of Reentrancies in Abstract Meaning Representation Parsing

Ida Szubert, Marco Damonte, Shay B. Cohen, Mark Steedman


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
Anthology ID:
2020.findings-emnlp.199
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2198–2207
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.199
DOI:
10.18653/v1/2020.findings-emnlp.199
Bibkey:
Cite (ACL):
Ida Szubert, Marco Damonte, Shay B. Cohen, and Mark Steedman. 2020. The Role of Reentrancies in Abstract Meaning Representation Parsing. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2198–2207, Online. Association for Computational Linguistics.
Cite (Informal):
The Role of Reentrancies in Abstract Meaning Representation Parsing (Szubert et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.199.pdf
Optional supplementary material:
 2020.findings-emnlp.199.OptionalSupplementaryMaterial.zip
Code
 mdtux89/amr-reentrancies