Thu, Oct 07, 2021 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Pei Zhou , USC/ISI
Talk Title: ROBUST AND IMPLICIT COMMONSENSE INFERENCE FOR SMOOTH COMMUNICATION
Series: NL Seminar
Abstract: REMINDER: Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you're highly encouraged to use your USC account to sign into Zoom. If you're an outside visitor, please inform nlg DASH seminar DASH host AT isi.edu beforehand so we'll be aware of your attendance and let you in.
Smooth and effective communication requires the ability to make implicit commonsense inferences that are robust to paraphrases. In this talk, I will mainly introduce my work on examining whether pre trained language models PTLMs can perform robust commonsense inferences and whether response generation RG models understand why a response sounds coherent. I will briefly present my other work on learning common sense in dialogue response generation.
In the pursuit of advancing fluid human AI communication, we first propose a new challenge, RICA Robust Inference using Commonsense Axioms, that evaluates robust commonsense inference despite textual perturbations. RICA consists of a set of natural language statements in the premise conclusion format that require reasoning using latent implicit commonsense relationships. We formulate these abstract commonsense relations between entities in first order logic and refer to them as commonsense axioms.
We also introduce CEDAR Common Sense in Dialogue Response Generation. CEDAR is a probing framework that aims to understand why RG models respond as they do by probing RG models understanding of commonsense reasoning that elicits proper responses. We formalize the problem by framing commonsense as a latent variable in the RG task and using explanations for responses as textual form of commonsense.
Biography: Pei Zhou is a third year Ph.D. student in Computer Science at the University of Southern California USC and Information Sciences Institute ISI co advised by Professors Xiang Ren and Jay Pujara. Pei graduated with a Bachelor of Science degree in Mathematics of Computation from UCLA in 2019, where he worked closely with Profs. Kai-Wei Chang and Yizhou Sun. In summers of 2021 and 2020, Pei interned as an applied scientist at Amazon Alexa AI, dialogue modeling team. Pei's current research focus lies in commonsense reasoning in dialogue response generation. He is also broadly interested in knowledge grounding in language, robustness, and fairness in NLP.
Host: Jon May and Thamme Gowda
More Info: https://nlg.isi.edu/nl-seminar/
WebCast Link: https://www.youtube.com/watch?v=Gx1wKxqRy1c
Audiences: NL Seminar
Contact: Pete Zamar