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# Hermann Ney

3 paper cards in Mova's library · 2006–2016

Hermann Ney wrote 3 of the papers Mova reads, in 2006–2016, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/); often with Adrià de Gispert, David Vilar, Deepa Gupta.

[OpenAlex](https://openalex.org/A5112501010) · [**h-index 94**](https://openalex.org/authors/A5112501010) · [48282 citations](https://openalex.org/authors/A5112501010) · [1461 works](https://openalex.org/works?filter=author.id:A5112501010) — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Hermann+Ney) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Hermann+Ney) · [dblp](https://dblp.org/search?q=Hermann+Ney) · [ACL Anthology](https://aclanthology.org/search/?q=Hermann+Ney) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Hermann+Ney)

Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 3

## Papers

1. **[CharacTer: Translation Edit Rate on Character Level](https://md.mova.elamur.ai/free/en/docs/papers/wang-2016-character-translation-edit-rate-character/)** *(2016)* CharacTER is a translation edit rate computed on characters. It keeps the word-level phrase shift of TER, so a moved phrase costs something, but the edit distance after the shifts is a character-level Levenshtein distance, with spaces counted as characters. Shifting characters directly was about 44 times slower, so…
2. **[Error Analysis of Statistical Machine Translation Output](https://md.mova.elamur.ai/free/en/docs/papers/vilar-2006-error-analysis-statistical-machine-translation/)** *(2006)* The paper gives a hand-built error typology for statistical MT output and applies it to the RWTH system in the first TC-STAR evaluation, for Spanish-to-English, English-to-Spanish and Chinese-to-English, on speech-derived data (clean text, verbatim transcripts and recogniser output).
3. **[Morpho-syntactic Information for Automatic Error Analysis of Statistical Machine Translation Output](https://md.mova.elamur.ai/free/en/docs/papers/popovic-2006-morpho-syntactic-information-automatic-error/)** *(2006)* The paper uses word classes to find out where an SMT system goes wrong, for Spanish and English in the TC-STAR parliament data.

## Co-authors (13)

- Adrià de Gispert
- David Vilar
- Deepa Gupta
- Hendrik Rosendahl
- Jan-Thorsten Peter
- Jia Xu
- José B. Mariño
- Luis Fernando D'Haro
- [Maja Popović](https://md.mova.elamur.ai/free/en/docs/papers/authors/maja-popovic/)
- Marcello Federico
- Patrik Lambert
- Rafael Banchs
- Weiyue Wang
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