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Fast decoding and optimal decoding for machine translation
Open accessAbstract
A good decoding algorithm is critical to the success of any statistical machine translation system. The decoder's job is to find the translation that is most likely according to set of previously learned parameters (and a formula for combining them). Since the space of possible translations is extremely large, typical decoding algorithms are only able to examine a portion of it, thus risking to miss good solutions. In this paper, we compare the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem.
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Cited by
A phrase-based, joint probability model for statistical machine translation
Marcu, D., & Wong, W. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118711 · Cited by 470
Self-citationA Survey of Paraphrasing and Textual Entailment Methods
Androutsopoulos, I., & Malakasiotis, P. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2985 · Cited by 178
Metadata
- Authors
- Germann, U., Jahr, M., Knight, K., Marcu, D., & Yamada, K.
- Venue
- Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01
- Published
- 2001
- Pages
- 228-235
- OpenAlex
- W2139403546
- Cited by
- 235
- References
- 0
- FWCI
- 11.17
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Fast decoding and optimal decoding for machine translation. https://app.cochat.ai/papers/10.3115/1073012.1073042
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- Germann, U., Jahr, M., Knight, K., Marcu, D., & Yamada, K. (2001). Fast decoding and optimal decoding for machine translation. Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01. https://doi.org/10.3115/1073012.1073042
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