ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

A tagger-aided language model with a stack decoder

Ruiqiang Zhang, Ezra Black, Andrew Finch, Yoshinori Sagisaka

This contribution of this paper is to investigate the utility of exploiting words and predicted detailed semantic tags in the long history to enhance a standard trigram language model. The paper builds on earlier work in the field that also used words and tags in the long history, but offers a cleaner, and ultimately much more accurate system by integrating the application of these new features directly into the decoding algorithm. The features used in our models are derived using a set of complex questions about the tags and words in the history, written by a linguist. Maximum entropy modelling techniques are then used to com- bine these features with a standard trigram language model. We evaluate the technique in terms of word error rate, on Wall Street Journal test data.


Cite as: Zhang, R., Black, E., Finch, A., Sagisaka, Y. (2000) A tagger-aided language model with a stack decoder. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 1, 250-253

@inproceedings{zhang00b_icslp,
  author={Ruiqiang Zhang and Ezra Black and Andrew Finch and Yoshinori Sagisaka},
  title={{A tagger-aided language model with a stack decoder}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 1, 250-253}
}