Comparative Quality Estimation for Machine Translation Observations on Machine Learning and Features
A deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier...
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| Format: | Article |
| Language: | English |
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Sciendo
2017-06-01
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| Series: | Prague Bulletin of Mathematical Linguistics |
| Online Access: | https://doi.org/10.1515/pralin-2017-0029 |
| Summary: | A deeper analysis on Comparative Quality Estimation is presented by extending the state-of-the-art methods with adequacy and grammatical features from other Quality Estimation tasks. The previously used linear method, unable to cope with the augmented features, is replaced with a boosting classifier assisted by feature selection. The methods indicated show improved performance for 6 language pairs, when applied on the output from MT systems developed over 7 years. The improved models compete better with reference-aware metrics. |
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| ISSN: | 1804-0462 |