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Survey of Abstract Meaning Representation: Then, Now, Future

What it is about

A survey of AMR in three parts. It covers the representation and its extensions. It covers evaluation, from Smatch and its variants (S2match, SMATCH++, the granular GrAPES suite) to SemBLEU, and the known weaknesses of each. It covers parsing, from alignment-based and transition-based methods to neural sequence and graph-to-graph models. It also covers AMR-to-text generation and applications such as text classification and information seeking. The sections on multilingual AMR list annotation projection through word alignments, parsing by machine translation into English followed by an English parser, multilingual transformer parsers, and the small non-English corpora: the Chinese translation of The Little Prince, a Turkish literary corpus with sentences aligned to their translations, and a four-translation subset of AMR 2.0. The survey gives scores from the literature (for example, 86.1 Smatch on AMR 2.0 for one recent parser). It runs no new experiments.

Ideas

For Mova

The ideas touch tools/translate/measure.rs (the per-class report), tools/translate/hebrew.rs (the Hebrew alignment), and tools/roundtrip (the graph comparison and the divergence classes). The survey is a reading list for the translator's evaluation, not a source of methods to copy. Its figures come from other papers and are not Mova results.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. arXiv 2505.03229

@misc{mansouri2025survey,
  title = {{Survey of Abstract Meaning Representation: Then, Now, Future}},
  author = {Mansouri, Behrooz},
  year = {2025},
  eprint = {2505.03229},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2505.03229}
}

References

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