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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; often with Adrià de Gispert, Aljoscha Burchardt, Daniel Zeman.

OpenAlex · ORCID · h-index 22 · 2989 citations · 119 works — 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 · Semantic Scholar · dblp · ACL Anthology · ORCID

Topics: Machine translation 6

Papers

  1. chrF++: words helping character n-grams (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 (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 (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 (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 (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 (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)

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