The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Minimum error rate training in statistical machine translation
Open accessAbstract
Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality. These training criteria make use of recently proposed automatic evaluation metrics. We describe a new algorithm for efficient training an unsmoothed error count. We show that significantly better results can often be obtained if the final evaluation criterion is taken directly into account as part of the training procedure.
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Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
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Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
Doddington, G. (2002) · Proceedings of the second international conference on Human Language Technology Research - · doi:10.3115/1289189.1289273 · Cited by 1,584
Discriminative training and maximum entropy models for statistical machine translation
Och, F. J., & Ney, H. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073133 · Cited by 1,034
Self-citationPattern Classification and Scene Analysis.
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Improved Alignment Models for Statistical Machine Translation
Och, F. J., Tillmann, C., & Ney, H. (1999) · Cited by 478
Self-citationAn Evaluation Tool for Machine Translation: Fast Evaluation for MT Research
Nießen, S., Och, F. J., Leusch, G., & Ney, H. (2000) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/56t9t4rchhar · Cited by 280
Self-citationAccelerated DP based search for statistical translation
Tillmann, C., Vogel, S., Ney, H., Zubiaga, A., & Sawaf, H. (1997) · 5th European Conference on Speech Communication and Technology (Eurospeech 1997) · doi:10.21437/eurospeech.1997-673 · Cited by 232
Parsing algorithms and metrics
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Cited by
Moses: open source toolkit for statistical machine translation
Koehn, P., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., et al. (2007) · Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions - ACL '07 · doi:10.3115/1557769.1557821 · Cited by 4,735
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
A Diversity-Promoting Objective Function for Neural Conversation Models
Li, J., Galley, M., Brockett, C., Gao, J., & Dolan, B. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1014 · Cited by 1,926
Domain adaptation with structural correspondence learning
Blitzer, J., McDonald, R., & Pereira, F. (2006) · Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06 · doi:10.3115/1610075.1610094 · Cited by 1,543
Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments
Lavie, A., & Agarwal, A. (2007) · Proceedings of the Second Workshop on Statistical Machine Translation - StatMT '07 · doi:10.3115/1626355.1626389 · Cited by 1,047
Metadata
- Authors
- Och, F. J.
- Venue
- Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03
- Published
- 2003
- Volume
- 1
- Pages
- 160-167
- OpenAlex
- W2146574666
- Cited by
- 2,725
- References
- 11
- FWCI
- 39.03
- Type
- conference paper
- Language
- en
- Open access
- bronze
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