Abstract
In this paper we describe two new objective automatic evaluation methods for machine translation. The first method is based on longest common subsequence between a candidate translation and a set of reference translations. Longest common subsequence takes into account sentence level structure similarity naturally and identifies longest co-occurring insequence n-grams automatically. The second method relaxes strict n-gram matching to skipbigram matching. Skip-bigram is any pair of words in their sentence order. Skip-bigram cooccurrence statistics measure the overlap of skip-bigrams between a candidate translation and a set of reference translations. The empirical results show that both methods correlate with human judgments very well in both adequacy and fluency. 1
Keywords
BigramComputer scienceMachine translationLongest common subsequence problemArtificial intelligenceSet (abstract data type)Natural language processingSubsequenceEvaluation of machine translationTranslation (biology)Matching (statistics)Similarity (geometry)Speech recognitionPattern matchingPattern recognition (psychology)AlgorithmTrigramStatisticsMathematicsExample-based machine translation
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Publication Info
- Year
- 2004
- Type
- article
- Pages
- 605-es
- Citations
- 708
- Access
- Closed
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Cite This
Chin-Yew Lin,
Franz Josef Och
(2004).
Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics.
, 605-es.
https://doi.org/10.3115/1218955.1219032
Identifiers
- DOI
- 10.3115/1218955.1219032