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  • CS Colloquium: Peng Qi (Stanford University) - Explainable and Efficient Knowledge Acquisition from Text

    Wed, Mar 04, 2020 @ 11:00 AM - 12:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Peng Qi, Stanford University

    Talk Title: Explainable and Efficient Knowledge Acquisition from Text

    Series: CS Colloquium

    Abstract: Human languages have served as the media for our knowledge over generations. With the rise of the digital world, making use of the knowledge that is encoded in text has become unprecedentedly important yet challenging. In recent years, the NLP community has made great progress towards operationalizing textual knowledge by building accurate systems that answer factoid questions. However, largely relying on matching local text patterns, these systems fall short at their ability to perform complex reasoning, which limits our effective use of textual knowledge. To address this problem, I will first talk about two distinct approaches to enable NLP systems to perform multi-step reasoning that is explainable to humans, through extracting facts from natural language and answering multi-step questions directly from text. I will then demonstrate that beyond static question answering with factoids, true informativeness of answers stems from communication. To this end, I will show how we lay the foundation for reasoning about latent information needs in conversations to effectively exchange information beyond providing factoid answers.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Peng Qi is a Computer Science PhD student at Stanford University. His research interests revolve around building natural language processing systems that better bridge between humans and the large amount of textual information we are engulfed in. He is excited about building scalable and explainable AI systems, and has worked on extracting knowledge representations from text, question answering involving complex reasoning, and multi-lingual NLP.

    Host: Xiang Ren

    Location: Ronald Tutor Hall of Engineering (RTH) - 109

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

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