  The paper presents a study on the portability of statistical syntactic
knowledge in the framework of the structured language model (SLM). We
investigate the impact of porting SLM statistics from the Wall Street Journal
(WSJ) to the Air Travel Information System (ATIS) domain. We compare this
approach to applying the Microsoft rule-based parser (NLPwin) for the ATIS data
and to using a small amount of data manually parsed at UPenn for gathering the
intial SLM statistics. Surprisingly, despite the fact that it performs modestly
in perplexity (PPL), the model initialized on WSJ parses outperforms the other
initialization methods based on in-domain annotated data, achieving a
significant 0.4% absolute and 7% relative reduction in word error rate (WER)
over a baseline system whose word error rate is 5.8%; the improvement measured
relative to the minimum WER achievable on the N-best lists we worked with is
12%.
