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# A Study of Translation Edit Rate with Targeted Human Annotation

**Authors:** Matthew Snover, [Bonnie Dorr](https://md.mova.elamur.ai/free/en/docs/papers/authors/bonnie-dorr/), Rich Schwartz, Linnea Micciulla, John Makhoul · **Year:** 2006 · **Venue:** AMTA 2006 (7th Conference of the Association for Machine Translation in the Americas) **Link:** [aclanthology.org/2006.amta-papers.25](https://aclanthology.org/2006.amta-papers.25/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**1514** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/snover-2006-study-translation-edit-rate-targeted/) of the 140673 papers whose bibliographies we read[Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers](https://aclanthology.org/volumes/2006.amta-papers/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/2006.amta-papers.25/)
- [PDF](https://aclanthology.org/2006.amta-papers.25.pdf)

**Authors (5)**

1. Matthew Snover
2. Bonnie Dorr
3. Rich Schwartz
4. Linnea Micciulla
5. John Makhoul

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

## What it is about

The paper proposes Translation Edit Rate (TER): the minimum number of edits (insertion, deletion, substitution, and shift of a word sequence, each costing one) that turns a system output into one of the references, divided by the average reference length. Finding the minimum with shifts is NP-complete, so the authors use a greedy search for shifts and an exact edit distance for the rest. A variant, HTER, first has a fluent native speaker edit the system output into a new reference with the same meaning, and then measures TER against that single reference. On 100 Arabic sentences with two systems, one targeted reference gave an edit rate of about 33.5 against about 49.6 for TER with four untargeted references, a reduction of about a third. HTER also correlated better with human fluency and adequacy judgments than BLEU did. Annotators spent three to seven minutes per sentence, and the authors note that a second check pass changed 0.63 words per sentence on average.

## Ideas

- I1. Add a TER-style edit count with greedy shifts to `tools/translate/src/measure.rs`. It gives a word-level distance between the stack output and the reference, and its edit script is more informative than a single rate.
- I2. Treat the edit script as an error source. Insertions, deletions, substitutions and shifts map onto the translator's residual classes (missing and extra words, lexical choice, word order). A count of shifts tests the order class directly.
- I3. For a few hundred dev verses, have a native speaker edit the output into a fluent reference, as HTER does. Use it only on dev, never on the test split, which is read once per release.
- I4. Write the annotation rule down before editing starts. The paper warns that editors who only copy a reference make the metric look worse; the rule (fix what the metric counts, keep meaning) should be a fixed file.

## For Mova

The code is `tools/translate/src/measure.rs`, with the edit script read out for the residual report. The HTER idea gives the translator a reference that is fluent and close to the output, which the Bible references are not, since their English is archaic (open problem 6). It is a costly human step, so it is a dev-only check for a small set of verses.

Topics: [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{snover2006study,
  title = {{A Study of Translation Edit Rate with Targeted Human Annotation}},
  author = {Snover, Matthew and Dorr, Bonnie and Schwartz, Rich and Micciulla, Linnea and Makhoul, John},
  booktitle = {Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers},
  year = {2006},
  url = {https://aclanthology.org/2006.amta-papers.25/}
}
```

- [← previous Quasi-Synchronous Grammars: Alignment by Soft Projection of Syntactic Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/smith-2006-quasi-synchronous-grammars-alignment-soft/)
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## References

13 references, 1 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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