Minimum Bayes-Risk Word Alignments of Bilingual Texts
By Shankar Kumar, William J. Byrne. 2002
74 citations recorded by the source corpus.
We present Minimum Bayes-Risk word alignment for machine translation. This statistical, model-based approach attempts to minimize the expected risk of alignment errors under loss functions that measure alignment quality. We describe various loss functions, including some that incorporate linguistic analysis as can be obtained from parse trees, and show that these approaches can improve alignments of the English-French Hansards.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Shankar Kumar
- Franz Joseph Och
- William J. Byrne
- David Chiang
- Li Deng
- Philipp Koehn
- Jianfeng Gao
- Ondrej Bojar
Citations in the corpus
- Minimum Error Rate Training in Statistical Machine Translation (2003, methodology): “In the field of natural language processing this approach has been applied for example in parsing (Goodman, 1996) and word alignment (Kumar and Byrne, 2002).”
- Minimum Bayes-Risk Decoding for Statistical Machine Translation (2004, methodology): “We apply the Minimum Bayes-Risk (MBR) techniques developed for automatic speech recognition (Goel and Byrne, 2000) and bitext word alignment for statistical MT (Kumar and Byrne, 2002), to the problem of building automatic MT systems tuned for specific metrics.”