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Towards Heterogeneous Automatic MT Error Analysis

What it is about

The paper argues that automatic MT metrics should be used for error analysis, not only for a single score. It compiles a set of metric variants at five linguistic levels: lexical (WER, PER, BLEU, NIST, GTM, ROUGE, METEOR), shallow syntax, dependency and constituency overlap, shallow semantics (named entities and semantic roles), and discourse representations. The variants are computed in the IQMT framework. The case study uses the NIST 2005 Arabic–English test set, with one human-aided system and the best statistical system. Surface metrics prefer the statistical system, while deeper metrics give rankings closer to human judgments. Reports are produced at document and sentence level, showing at which level a system loses. There is no formal study of how reliable the reports are.

Ideas

For Mova

The places are tools/translate/src/measure.rs, tools/roundtrip (frames), prag (roles) and tools/macula (Hebrew trees). The metric variants themselves are not needed, since Mova's frames are deterministic. What carries over is the level-by-level report. Nothing here has been tried in Mova.

Topics: Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.63317/3fq8iorodjww

@inproceedings{gimenez2008heterogeneous,
  title = {{Towards Heterogeneous Automatic MT Error Analysis}},
  author = {Giménez, Jesús and Màrquez, Lluís},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  doi = {10.63317/3fq8iorodjww},
  url = {https://doi.org/10.63317/3fq8iorodjww}
}

References

27 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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