Fri, Mar 31, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Danqi Chen, Stanford Univ.
Talk Title: Towards the Machine Comprehension of Text
Series: Natural Language Seminar
Abstract: In this talk, I will first present how we advance this line of research. I will show how simple models can achieve nearly state of the art performance on recent benchmarks, including the CNN Daily Mail datasets and the Stanford Question Answering Dataset. I will focus on explaining the logical structure behind these neural architectures and discussing advantage as well as limits of current approaches. Lastly I will talk about our recent work on scaling up machine comprehension systems, which attempt to answer open domain questions at the full Wikipedia scale. We demonstrate the promise of our system, as well as set up new benchmarks by evaluating on multiple existing QA datasets.
Biography: Danqi Chen is a PhD candidate in Computer Science at Stanford University, advised by Professor Christopher Manning. Her main research interests lie in deep learning for natural language processing and understanding, and she is particularly interested in the intersection between text understanding and knowledge reasoning. She has been working on machine comprehension, question answering, knowledge base population and dependency parsing. She is a recipient of Facebook fellowship and Microsoft Research Womens Fellowship and an outstanding paper award in ACL 16. Prior to Stanford, she received her BS from Tsinghua University.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Audiences: Everyone Is Invited
Contact: Peter Zamar