Assessing the Quality of MT Systems for Hindi to English Translation
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TLDR
This paper evaluated the translation quality of different MT engines for Hindi-English (Hindi data is provided as input and English is obtained as output) using various automatic metrics like BLEU, METEOR etc.Abstract:
Evaluation plays a vital role in checking the quality of MT output. It is done either manually or automatically. Manual evaluation is very time consuming and subjective, hence use of automatic metrics is done most of the times. This paper evaluates the translation quality of different MT Engines for Hindi-English (Hindi data is provided as input and English is obtained as output) using various automatic metrics like BLEU, METEOR etc. Further the comparison automatic evaluation results with Human ranking have also been given. General Terms Machine Translation, Natural Language Processing.read more
Citations
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References
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Bleu: a Method for Automatic Evaluation of Machine Translation
TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Proceedings Article
METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments
Satanjeev Banerjee,Alon Lavie +1 more
TL;DR: METEOR is described, an automatic metric for machine translation evaluation that is based on a generalized concept of unigram matching between the machineproduced translation and human-produced reference translations and can be easily extended to include more advanced matching strategies.
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Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
TL;DR: NIST commissioned NIST to develop an MT evaluation facility based on the IBM work, which is now available from NIST and serves as the primary evaluation measure for TIDES MT research.
Proceedings ArticleDOI
Manual and Automatic Evaluation of Machine Translation between European Languages
Philipp Koehn,Christof Monz +1 more
TL;DR: This work evaluated machine translation performance for six European language pairs that participated in a shared task: translating French, German, Spanish texts to English and back.
Book ChapterDOI
The significance of recall in automatic metrics for MT evaluation
TL;DR: This work shows that correlation with human judgments is highest when almost all of the weight is assigned to recall, and shows that stemming is significantly beneficial not just to simpler unigram precision and recall based metrics, but also to BLEU and NIST.
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