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# MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility based on semantic roles

**Authors:** [Chi-kiu Lo](https://md.mova.elamur.ai/free/en/docs/papers/authors/chi-kiu-lo/), [Dekai Wu](https://md.mova.elamur.ai/free/en/docs/papers/authors/dekai-wu/) · **Year:** 2011 · **Venue:** Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies **Link:** [aclanthology.org/P11-1023](https://aclanthology.org/P11-1023/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**47** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lo-2011-meant-inexpensive-high-accuracy-semi/) of the 140673 papers whose bibliographies we read[Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies](https://aclanthology.org/volumes/P11-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/P11-1023/)
- [PDF](https://aclanthology.org/P11-1023.pdf)

**Authors (2)**

1. Chi-kiu Lo
2. Dekai Wu

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

MEANT judges translation utility, meaning whether a reader can recover who did what to whom. For each predicate in the reference and in the output it aligns predicates by lexical similarity, aligns the role fillers of each aligned pair by weighted bipartite matching, and computes a weighted F-score over the matched role-filler pairs. Frames are labelled either by untrained monolingual annotators (the human version, HMEANT) or by an automatic shallow semantic parser (the automatic version). On sentence-level adequacy judgments, HMEANT reaches a correlation of 0.43, the same as HTER and far above BLEU at 0.20. The automatic version reaches 0.34, about 80% of HTER, at a much lower labour cost. The study is a meta-evaluation over WMT-style data.

## Ideas

- I1. A frame-level score for translator output in normal mode. For each verse, the frames of the output (predicate, roles from `prag`) are matched against the frames read from the reference by the same reader, and a weighted F-score per verse is reported. This gives an adequacy number without a parser on the output.
- I2. Implement the bipartite matching for role fillers deterministically: Hungarian assignment with ties broken by index, and candidates held in ordered maps, so two runs agree.
- I3. Use the predicate alignment as a check on the lexicon (open problem 2). When a concept merge makes two predicates align that the frames treat differently, the merge is counted as an error.

## For Mova

The places are `prag` (frames), `tools/roundtrip`, and `tools/translate/src/measure.rs`. The paper's frames come from a learned SRL parser. Mova has deterministic frames, so the transferable part is the matching and the weighted score, not the parser. Measure first on the dev verses, then run the same check on the held-out verses once.

Topics: [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/) · [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/)

## Cite

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

```
@inproceedings{lo2011meant,
  title = {{MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility based on semantic roles}},
  author = {Lo, Chi-kiu and Wu, Dekai},
  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
  year = {2011},
  url = {https://aclanthology.org/P11-1023/}
}
```

- [← previous Text Generation from Discourse Representation Structures](https://md.mova.elamur.ai/free/en/docs/papers/liu-2021-text-generation-discourse-representation-structures/)
- [next → MEANT 2.0: Accurate semantic MT evaluation for any output language](https://md.mova.elamur.ai/free/en/docs/papers/lo-2017-meant-2-0-accurate-semantic/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

## References

20 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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