Strategies for Statistical Spoken Language Understanding with Small Amount of Data – an Empirical Study

Ye-Yi Wang

Abstract

The semantic frame based spoken language understanding involves

two decisions – frame classification and slot filling. The

two decisions can be made either separately or jointly. This

paper compares the different strategies and presents some empirical

results in the conditional model framework when only a

small amount of training data is available. It is found that while

the two pass classification/slot filling solution has resulted in

the much better frame classification accuracy, the joint model

has yielded better results for slot filling. Application developers

need to carefully choose the strategy appropriate to the application

scenarios.

Details

Publication typeInproceedings
Published inProc. of Interspeech
PublisherInternational Speech Communication Association
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