Title :
Portability of syntactic structure for language modeling
Author_Institution :
Microsoft Speech.Net/Res., Redmond, WA, USA
Abstract :
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 (NLP-win) for the ATIS data and to using a small amount of data manually parsed at UPenn for gathering the initial 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%
Keywords :
grammars; natural languages; parameter estimation; probability; ATIS; Air Travel Information System; Microsoft rule-based parser; NLP-win; UPenn; Wall Street Journal; language modeling; perplexity; portability; statistical syntactic knowledge; syntactic structure; Error analysis; Information systems; Performance evaluation; Statistics; Training data;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.940888