Statistical natural language understanding using hidden clumpings

Authors:
M. Epstein;K. Papineni;S. Roukos;T. Ward;S. Della Pietra
Affiliations:
IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA;IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA;IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA;IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA;IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA
Venue:
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Year:
1996

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Abstract

We present a new approach to natural language understanding (NLU) based on the source-channel paradigm, and apply it to ARPA's Air Travel Information Service (ATIS) domain. The model uses techniques similar to those used by IBM in statistical machine translation. The parameters are trained using the exact match algorithm; a hierarchy of models is used to facilitate the bootstrapping of more complex models from simpler models.