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  • CS Colloquium: Hal Daumé III (UMD) - Learning Language through Interaction

    Mon, Nov 14, 2016 @ 12:00 PM - 01:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Hal Daumé III, UMD

    Talk Title: Learning Language through Interaction

    Series: Yahoo! Labs Machine Learning Seminar Series

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium. Part of Yahoo! Labs Machine Learning Seminar Series.

    Machine learning-based natural language processing systems are amazingly effective, when plentiful labeled training data exists for the task/domain of interest. Unfortunately, for broad coverage (both in task and domain) language understanding, we're unlikely to ever have sufficient labeled data, and systems must find some other way to learn. I'll describe a novel algorithm for learning from interactions, and several problems of interest, most notably machine simultaneous interpretation (translation while someone is still speaking). This is all joint work with some amazing (former) students He He, Alvin Grissom II, John Morgan, Mohit Iyyer, Sudha Rao and Leonardo Claudino, as well as colleagues Jordan Boyd-Graber, Kai-Wei Chang, John Langford, Akshay Krishnamurthy, Alekh Agarwal, Stéphane Ross, Alina Beygelzimer and Paul Mineiro.

    Biography: Hal Daume III is an associate professor in Computer Science at the University of Maryland, College Park. He holds joint appointments in UMIACS and Linguistics. He was previously an assistant professor in the School of Computing at the University of Utah. His primary research interest is in developing new learning algorithms for prototypical problems that arise in the context of language processing and artificial intelligence. This includes topics like structured prediction, domain adaptation and unsupervised learning; as well as multilingual modeling and affect analysis. He associates himself most with conferences like ACL, ICML, NIPS and EMNLP. He earned his PhD at the University of Southern California with a thesis on structured prediction for language (his advisor was Daniel Marcu). He spent the summer of 2003 working with Eric Brill in the machine learning and applied statistics group at Microsoft Research. Prior to that, he studied math (mostly logic) at Carnegie Mellon University.

    Host: Yan Liu

    Location: Seeley G. Mudd Building (SGM) - 123

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair


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