BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Some Improvements over the BLEU Metric for Measuring Translation Quality for Hindi
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
HLT '02 Proceedings of the second international conference on Human Language Technology Research
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Word error rates: decomposition over Pos classes and applications for error analysis
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
(Meta-) evaluation of machine translation
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Fluency, adequacy, or HTER?: exploring different human judgments with a tunable MT metric
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
Morpho-syntactic information for automatic error analysis of statistical machine translation output
StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
Error detection for statistical machine translation using linguistic features
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Towards automatic error analysis of machine translation output
Computational Linguistics
TerrorCat: a translation error categorization-based MT quality metric
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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This paper addresses diagnostic evaluation of machine translation (MT) systems for Indian languages, English to Hindi MT in particular, assessing the performance of MT systems on relevant linguistic phenomena (checkpoints). We use the diagnostic evaluation tool DELiC4MT to analyze the performance of MT systems on various PoS categories (e.g. nouns, verbs). The current system supports only word level checkpoints which might not be as helpful in evaluating the translation quality as compared to using checkpoints at phrase level and checkpoints that deal with named entities (NE), inflections, word order, etc. We therefore suggest phrase level checkpoints and NEs as additional checkpoints for DELiC4MT. We further use Hjerson to evaluate checkpoints based on word order and inflections that are relevant for evaluation of MT with Hindi as the target language. The experiments conducted using Hjerson generate overall (document level) error counts and error rates for five error classes (inflectional errors, reordering errors, missing words, extra words, and lexical errors) to take into account the evaluation based on word order and inflections. The effectiveness of the approaches was tested on five English to Hindi MT systems.