Sixth International Conference on Spoken Language Processing (ICSLP 2000)
October 16-20, 2000
A Tagger-Aided Language Model with a Stack Decoder
Ruiqiang Zhang, Ezra Black, Andrew Finch, Yoshinori Sagisaka
ATR Spoken Language Translation Laboratories,
Soraku-gun, Kyoto, Japan
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.
Zhang, Ruiqiang / Black, Ezra / Finch, Andrew / Sagisaka, Yoshinori (2000):
"A tagger-aided language model with a stack decoder",
In ICSLP-2000, vol.1, 250-253.