[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/maja-popovic/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Maja Popović # Maja Popović 6 paper cards in Mova's library · 2006–2017 Maja Popović wrote 6 of the papers Mova reads, in 2006–2017, most of them on [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/); often with Adrià de Gispert, [Aljoscha Burchardt](https://md.mova.elamur.ai/free/en/docs/papers/authors/aljoscha-burchardt/), [Daniel Zeman](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-zeman/). [OpenAlex](https://openalex.org/A5045306408) · [ORCID](https://orcid.org/0000-0001-8234-8745) · [**h-index 22**](https://openalex.org/authors/A5045306408) · [2989 citations](https://openalex.org/authors/A5045306408) · [119 works](https://openalex.org/works?filter=author.id:A5045306408) — 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=Maja+Popovi%C4%87) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Maja+Popovi%C4%87) · [dblp](https://dblp.org/search?q=Maja+Popovi%C4%87) · [ACL Anthology](https://aclanthology.org/search/?q=Maja+Popovi%C4%87) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Maja+Popovi%C4%87) Topics: [Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/topics/machine-translation/) 6 ## Papers 1. **[chrF++: words helping character n-grams](https://md.mova.elamur.ai/free/en/docs/papers/popovic-2017-chrf-words-helping-character-n/)** *(2017)* The paper asks whether word n-grams add anything to the character chrF score, and whether chrF tracks direct human assessment (absolute scores per segment) as well as it tracks the relative rankings used earlier. 2. **[chrF deconstructed: beta parameters and n-gram weights](https://md.mova.elamur.ai/free/en/docs/papers/popovic-2016-chrf-deconstructed-beta-parameters-n/)** *(2016)* The paper examines the two free settings of chrF: the β parameter and the weights of the n-gram orders. It sweeps β from 1/6 (precision-heavy) to 6 (recall-heavy) and computes segment-level Kendall's tau against human rankings on the WMT 2014 and WMT 2015 outputs, for English and several other target languages. It… 3. **[chrF: character n-gram F-score for automatic MT evaluation](https://md.mova.elamur.ai/free/en/docs/papers/popovic-2015-chrf-character-n-gram-f/)** *(2015)* The paper proposes chrF, an F-score over character n-grams. Precision is the share of hypothesis character n-grams found in the reference, recall is the share of reference n-grams found in the hypothesis, both averaged over n-gram lengths up to 6, and a parameter β gives recall β times the weight of precision. The… 4. **[Automatic MT Error Analysis: Hjerson Helping Addicter](https://md.mova.elamur.ai/free/en/docs/papers/berka-2012-automatic-mt-error-analysis-hjerson/)** *(2012)* The paper presents an open-source tool for detailed MT error analysis. It joins two systems: Hjerson, the automatic classifier from Popović (2011), and Addicter, which brings alignment, a web interface and a corpus browser. Errors are found by comparing the output with the reference through a chosen word alignment… 5. **[From Human to Automatic Error Classification for Machine Translation Output](https://md.mova.elamur.ai/free/en/docs/papers/popovic-2011-human-automatic-error-classification-machine/)** *(2011)* The paper turns a manual error typology into an automatic classifier. The classifier uses the word-level alignment behind WER and two position-independent error rates, one computed on the reference (words of the reference missing from the output) and one on the hypothesis (extra words in the output). Each error is… 6. **[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 (12) - Adrià de Gispert - [Aljoscha Burchardt](https://md.mova.elamur.ai/free/en/docs/papers/authors/aljoscha-burchardt/) - [Daniel Zeman](https://md.mova.elamur.ai/free/en/docs/papers/authors/daniel-zeman/) - Deepa Gupta - [Hermann Ney](https://md.mova.elamur.ai/free/en/docs/papers/authors/hermann-ney/) - Jan Berka - José B. Mariño - Marcello Federico - [Mark Fishel](https://md.mova.elamur.ai/free/en/docs/papers/authors/mark-fishel/) - [Ondřej Bojar](https://md.mova.elamur.ai/free/en/docs/papers/authors/ondrej-bojar/) - Patrik Lambert - Rafael Banchs - [← previous Mahmoud Reda](https://md.mova.elamur.ai/free/en/docs/papers/authors/mahmoud-reda/) - [next → Mamoru Komachi](https://md.mova.elamur.ai/free/en/docs/papers/authors/mamoru-komachi/) - [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)