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
- Young-Bum Kim, Karl Stratos, Ruhi Sarikaya, and Minwoo Jeong, New Transfer Learning Techniques For Disparate Label Sets, in Association for Computational Linguistics (ACL), ACL – Association for Computational Linguistics, August 2015.
- Young-Bum Kim, Minwoo Jeong, Karl Stratos, and Ruhi Sarikaya, Weakly Supervised Slot Tagging with Partially Labeled Sequences from Web Search Click Logs, in North American Chapter of the Association for Computational Linguistics (NAACL), ACL – Association for Computational Linguistics, June 2015.
- Tasos Anastasakos, Young-Bum Kim, and Anoop Deoras, Task specific continuous word representations for mono and multi-lingual spoken language understanding, in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, IEEE – Institute of Electrical and Electronics Engineers, May 2014.
- Larry Heck, Dilek Hakkani-Tur, Madhu Chinthakunta, Gokhan Tur, Rukmini Iyer, Partha Parthasarathy, Lisa Stifelman, Elizabeth Shriberg, and Ashley Fidler, Multimodal Conversational Search and Browse, IEEE Workshop on Speech, Language and Audio in Multimedia, August 2013.
- Young-Bum Kim and Benjamin Snyder, Optimal Data Set Selection: An Application to Grapheme-to-Phoneme Conversion, in North American Association for Computational Linguistics (ACL), ACL – Association for Computational Linguistics, June 2013.
- Malcolm Slaney, Pay Attention, Please: Attention at the Telluride Neuromorphic Cognition Workshop , in IEEE SLTC Newsletter, IEEE, November 2012.
- Young-Bum Kim and Benjamin Snyder, Universal Grapheme-to-Phoneme Prediction Over Latin Alphabets, in Empirical Methods in Natural Language Processing (EMNLP), ACL – Association for Computational Linguistics, July 2012.
- Young-Bum Kim and Benjamin Snyder, Universal Morphological Analysis using Structured Nearest Neighbor Prediction, in Empirical Methods in Natural Language Processing (EMNLP), ACL – Association for Computational Linguistics, July 2011.
- Jingjing Liu, Xiao Li, Alex Acero, and Ye-Yi Wang, Lexicon Modeling for Query Understanding, in ICASSP, IEEE, May 2011.
- Xiao Li, Understanding the Semantic Structure of Noun Phrase Queries, in ACL, Association for Computational Linguistics, July 2010.