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
- I1. A level profile for each translator output: score it per level (concept, tag, head and relation, frame role, word order) and print the level where the score falls. The profile replaces one residual number with a short diagnosis.
- I2. Keep the paper's warning as a rule. When a translator variant improves the exact-match score but not the frame-level score, the surface gain is flagged. A delta is kept only if the deeper-level score also improves.
- I3. A dependency-overlap score for subject and object pairs, computed on the meta trees of two readings of one verse. It measures open problem 1 directly: do the two readings agree on the heads?
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.
Cite
@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
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