Factored bilingual n-gram language models for statistical machine translation

In this work, we present an extension of n-gram-based translation models based on factored language models (FLMs). Translation units employed in the n-gram-based approach to statistical machine translation (SMT) are based on mappings of sequences of raw words, while translation model probabilities are estimated through standard language modeling of such bilingual units. Therefore, similar to other translation model approaches (phrase-based or hierarchical), the sparseness problem of the units being modeled leads to unreliable probability estimates, even under conditions where large bilingual corpora are available. In order to tackle this problem, we extend the n-gram-based approach to SMT by tightly integrating more general word representations, such as lemmas and morphological classes, and we use the flexible framework of FLMs to apply a number of different back-off techniques. In this work, we show that FLMs can also be successfully applied to translation modeling, yielding more robust probability estimates that integrate larger bilingual contexts during the translation process.