Understanding what users like to do/need to get is critical in human computer interaction. When natural user interface like speech or natural language is used in human-computer interaction, such as in a spoken dialogue system or with an internet search engine, language understanding becomes an important issue. Intent understanding is about identifying the action a user wants a computer to take or the information she/he would like to obtain, conveyed in a spoken utterance or a text query.
In this project, we develop robust data-driven technologies applicable to different domains, make them more practical by leveraging large amount of unlabeled data via unsupervised/semi-supervised machine learning; by innovating machine learning algorithms that work better with less data or mismatched data; and by augmenting statistical models with domain knowledge obtained in a semi-supervised fashion. Research activities fall into the following areas:
- Data-Driven Approaches to Spoken Language/Query Understanding
- Unsupervised/Semi-Supervised Learning
- Automatic/Semi-automatic Acquisition of Domain Knowledge
- Authoring Tools for Spoken Language Understanding
- Application of Intent Understanding Technology
We have contributed to Microsoft products from the following teams:
- Microsoft Live Search/Commerce Search
- Microsoft adCenter
- Microsoft Speech Component Group
- Tellme