Event Details

Natural language processing (NLP) has had increasing success and produced extensive industrial applications. Despite being sufficient to enable these applications, current NLP systems often ignore the social part of language, e.g., who says it, in what context, for what goals.  In this talk, we take a closer look at social factors in language via a new theory taxonomy and its interplay with computational methods via three lines of work.  The first one studies hate speech and racial bias by introducing a benchmark corpus on implicit hate speech and computational models on detecting and explaining latent hatred in language. We then look at what makes language persuasive by leveraging both weakly supervised methods and social science theories to understand persuasion strategies in good-faith requests. The last part demonstrates how more structures of conversations can be utilized to generate better summaries for everyday interaction.  We conclude by discussing several open-ended questions about how to build socially aware language technologies.

Speaker Bio

Diyi Yang is an assistant professor in the School of Interactive Computing at Georgia Tech, also affiliated with the Machine Learning Center (ML@GT), Institute for People and Technology (IPaT). She received her PhD from Carnegie Mellon University. Diyi has broad interests in Natural Language Processing and Computational Social Science, including dialogue summarization, limited data learning, bias and hate speech, and NLP for social good. Her work has received multiple best paper awards (or nominations) at EMNLP, ICWSM, SIGCHI, and CSCW. She is a Microsoft Research Faculty Fellow, a Forbes 30 under 30 in Science, an IEEE “AI 10 to Watch”, and a recipient of the Intel Rising Star Faculty Award.

Host: Xuezhe Ma, POC: Amy Feng

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