This paper describes a novel technique for incorporating syntactic knowledge into phrasebased machine translation through incremental syntactic parsing. 

Bottom-up and topdown parsers typically require a completed string as input. 

This requirement makes it difficult to incorporate them into phrase-based translation, which generates partial hypothesized translations from left-to-right. 

Incremental syntactic language models score sentences in a similar left-to-right fashion, 
and are therefore a good mechanism for incorporating syntax into phrase-based translation. 

We give a formal definition of one such linear-time syntactic language model, 
detail its relation to phrase-based decoding, and integrate the model with the Moses phrase-based translation system. 

We present empirical results on a constrained Urdu-English translation task that demonstrate a significant BLEU score improvement and a large decrease in perplexity.