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CS Distinguished Lecture: Rada Mihalcea (University of Michigan) - Moving Away from One-Size-Fits-All Natural Language Processing
The typical approach in natural language processing is to use one-size-fits-all representations, obtained from training one model on very large text collections. While this approach is effective for those people whose language style is well represented in the data, it fails to account for variations between people, and often leads to decreased performance for those in the minority. In this talk, I will challenge the one-size-fits-all assumption, and show that (1) we can identify words that are used in significantly different ways by speakers from different cultures; and (2) we can effectively use information about the people behind the words to build better natural language processing models.

This lecture satisfies requirements for CSCI 591: Research Colloquium.

Feb 25, 2021 04:00 PM in Pacific Time (US and Canada)

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