Abstract
Conventional statistical machine translation (SMT) approaches might not be able to find a good translation due to problems in its statistical models (due to data sparseness during the estimation of the model parameters) as well as search errors during the decoding process. This paper1 presents an example-based rescoring method that validates SMT translation candidates and judges whether the selected decoder output is good or not. Given such a validation filter, defective translations can be rejected. The experiments show a drastic improvement in the overall system performance compared to translation selection methods based on statistical scores only.
Cite
CITATION STYLE
Paul, M., Sumita, E., & Yamamoto, S. (2004). Example-based rescoring of statistical machine translation output. In HLT-NAACL 2004 - Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (pp. 9–12). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1613984.1613987
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