Effect of Source Language on AMR Structure

Shira Wein, Wai Ching Leung, Yifu Mu, Nathan Schneider


Abstract
The Abstract Meaning Representation (AMR) annotation schema was originally designed for English. But the formalism has since been adapted for annotation in a variety of languages. Meanwhile, cross-lingual parsers have been developed to derive English AMR representations for sentences from other languages—implicitly assuming that English AMR can approximate an interlingua. In this work, we investigate the similarity of AMR annotations in parallel data and how much the language matters in terms of the graph structure. We set out to quantify the effect of sentence language on the structure of the parsed AMR. As a case study, we take parallel AMR annotations from Mandarin Chinese and English AMRs, and replace all Chinese concepts with equivalent English tokens. We then compare the two graphs via the Smatch metric as a measure of structural similarity. We find that source language has a dramatic impact on AMR structure, with Smatch scores below 50% between English and Chinese graphs in our sample—an important reference point for interpreting Smatch scores in cross-lingual AMR parsing.
Anthology ID:
2022.law-1.12
Volume:
Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LAW
SIG:
SIGANN
Publisher:
European Language Resources Association
Note:
Pages:
97–102
Language:
URL:
https://aclanthology.org/2022.law-1.12
DOI:
Bibkey:
Cite (ACL):
Shira Wein, Wai Ching Leung, Yifu Mu, and Nathan Schneider. 2022. Effect of Source Language on AMR Structure. In Proceedings of the 16th Linguistic Annotation Workshop (LAW-XVI) within LREC2022, pages 97–102, Marseille, France. European Language Resources Association.
Cite (Informal):
Effect of Source Language on AMR Structure (Wein et al., LAW 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.law-1.12.pdf
Code
 shirawein/effect-language-amr-structure