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. Index Terms: spoken language understanding, classification, maximum entropy model, conditional random fields
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
