Incremental syntactic language models for phrase-based translation
Lane Schwartz, Chris Callison-Burch, William Schuler, Stephen Wu · 2011
This paper describes a novel technique for in-corporating syntactic knowledge into phrase-based machine translation through incremen-tal syntactic parsing. Bottom-up and top-down parsers typically require a completed string as input. This requirement makes it dif-ficult to incorporate them into phrase-based translation, which generates partial hypothe-sized translations from left-to-right. Incre-mental syntactic language models score sen-tences in a similar left-to-right fashion, and are therefore a good mechanism for incorporat-ing syntax into phrase-based translation. We give a formal definition of one such linear-time syntactic language model, detail its re-lation to phrase-based decoding, and integrate the model with the Moses phrase-based trans-lation system. We present empirical results on a constrained Urdu-English translation task that demonstrate a significant BLEU score im-provement and a large decrease in perplexity. 1