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Understand User's Intent from Speech and Text

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 indentifying 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 todifferent domains, make them morepractical 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 obtainedin 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 Undrestanding Technology

We have contributed to Microsoft products from the following teams:

  • Microsoft Live Search/Commerce Search
  • Microsoft adCenter
  • Microsoft Speech Component Group
  • Tellme