Large Scale Decipherment for Out-of-Domain Machine Translation
Qing Dou, Kevin K. Knight · 2012
We apply slice sampling to Bayesian decipherment and use our new decipherment framework to improve out-of-domain machine translation. Compared with the state of the art algorithm, our approach is highly scalable and produces better results, which allows us to decipher ciphertext with billions of tokens and hundreds of thousands of word types with high accuracy. We decipher a large amount of monolingual data to improve out-of-domain translation and achieve significant gains of up to 3.8 BLEU points. 1