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Assessing the Cross-linguistic Utility of Abstract Meaning Representation

What it is about

The article tests whether AMR, which was built for English, can serve as a cross-lingual meaning representation. It asks three questions. First, how much do the AMRs of parallel sentences differ? The authors transfer non-English tokens to English, then compare the graphs with Smatch. The source language changes AMR structure measurably, and the difference is smaller after translating to English first. Second, why do parallel AMRs differ? A new taxonomy sorts the causes into translation choice, annotation choice and inherent differences between languages. Third, does AMR see more than string-level similarity? Across English-Chinese, English-French and English-Spanish pairs, AMR picks up finer divergences than sentence embeddings. The authors conclude that AMR is useful as an interlingua but that evaluation must account for the source language, and that lexicalisation differences limit it.

Ideas

For Mova

The paper gives no ready numbers for Mova. The cause taxonomy and the token-transfer step are the useful parts. They bear on open problem 1 (reader attachment noise) and on the per-author delta design in tools/translate, where a delta must be kept only when it fixes enough tokens. The fine-grained comparison itself is in wein-2023-measuring-fine-grained-semantic-equivalence.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00503

@article{wein2024assessing,
  title = {{Assessing the Cross-linguistic Utility of Abstract Meaning Representation}},
  author = {Wein, Shira and Schneider, Nathan},
  journal = {Computational Linguistics},
  year = {2024},
  doi = {10.1162/coli\_a\_00503},
  url = {https://doi.org/10.1162/coli_a_00503}
}

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