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  • NL Seminar-Acquiring and Understanding Cross Task Generalization with Diverse NLP Tasks

    Thu, Oct 06, 2022 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Qinyuan Ye, USC/ISI

    Talk Title: Acquiring and Understanding Cross Task Generalization with Diverse NLP Tasks

    Series: NL Seminar

    Abstract: REMINDER
    Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you are highly encouraged to use your USC account to sign into Zoom.

    If you are an outside visitor, please inform us at nlg DASH seminar DASH host AT isi DOT edu beforehand so we will be aware of your attendance and let you in.

    In person attendance will be permitted for USC ISI faculty, staff, students only. Open to the public virtually via the zoom link and online.

    Humans can learn and perform a new language task more efficiently than machines, when they are provided with either task instructions or only a few examples for that task. We believe such learning efficiency is partly achieved by accumulating past learning experiences, i.e., learning to learn with previously seen tasks. We refer to such capability as cross task generalization and envision it to be an integral piece towards generalist NLP systems.

    In this talk, I will present our recent efforts in acquiring and understanding cross task generalization with diverse NLP tasks 1. To build a learning environment for acquiring and evaluating cross-task generalization, we construct NLP Few shot Gym, a repository of 160 few shot tasks collected from open access NLP datasets, converted to a unified text to text format, and covering diverse formats, goals and domains. We further introduce CrossFit, a few shot learning challenge that systematically evaluates an algorithms ability to quickly learn new tasks. With these resources, we conduct an empirical analysis with multi task learning and meta learning approaches, which provides fresh insights. 2. To better understand how models learn transferable skills to achieve cross task generalization, we develop task level mixture of expert models that explicitly emulates the behavior of accumulating skills and recomposing them when encountering a new task. Our empirical results suggest that training task level mixture of experts can alleviate negative transfer and achieve better few shot performance on unseen tasks; further we find that the learned routing decisions and experts partially rediscover human categorization of NLP tasks.

    Biography: Qinyuan Ye is a fourth year CS Ph.D. student at University of Southern California, advised by Prof. Xiang Ren. Her research interests lie in natural language processing. In particular she is interested in approaches that reduce human annotation efforts, including methods leveraging distant supervision, high level human supervision for example explanations, instructions, and metalearning. Prior to USC, she was an undergraduate student at Tsinghua University, majoring in Automation.

    Host: Jon May and Meryem M'hamdi

    More Info: https://nlg.isi.edu/nl-seminar/

    Webcast: https://www.youtube.com/watch?v=hpIohClvins

    Location: Information Science Institute (ISI) - Virtual Only

    WebCast Link: https://www.youtube.com/watch?v=hpIohClvins

    Audiences: Everyone Is Invited

    Contact: Pete Zamar


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