Conferences, Lectures, & Seminars
Events for October
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NL Seminar- ROBUST AND IMPLICIT COMMONSENSE INFERENCE FOR SMOOTH COMMUNICATION
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: https://www.youtube.com/watch?v=Gx1wKxqRy1cLocation: Information Science Institute (ISI) - Virtual
WebCast Link: https://www.youtube.com/watch?v=Gx1wKxqRy1c
Audiences: NL Seminar
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar-Chasing the Long Tail. What Neural Networks Memorize and Why
Thu, Oct 14, 2021 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Vitaly Feldman, Apple AI Research
Talk Title: Chasing the Long Tail: What Neural Networks Memorize and Why
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.
Deep learning algorithms that achieve state of the art results on image and text recognition tasks tend to fit the entire training dataset nearly perfectly including mislabeled examples and outliers. This propensity to memorize seemingly useless data and the resulting large generalization gap have puzzled many practitioners and is not explained by existing theories of machine learning. We provide a simple conceptual explanation and a theoretical model demonstrating that memorization of outliers and mislabeled examples is necessary for achieving close to optimal generalization error when learning from long tailed data distributions. Image and text data are known to follow such distributions and therefore our results establish a formal link between these empirical phenomena. We then demonstrate the utility of memorization and support our explanation empirically. These results rely on a new technique for efficiently estimating memorization and influence of training data points.
Biography: Vitaly Feldman is a research scientist at Apple AI Research working on foundations of machine learning and privacy preserving data analysis. His recent research interests include tools for analysis of generalization, distributed privacy preserving learning, privacy preserving optimization, and adaptive data analysis.
Vitaly holds a Ph.D. from Harvard 2006, advised by Leslie Valiant and was previously a research scientist at Google Research Brain Team and IBM Research Almaden. His work was recognized by the COLT Best Student Paper Award in 2005 and 2013 student co authored and by the IBM Research Best Paper Award in 2014, 2015 and 2016. His recent research on foundations of adaptive data analysis has been featured in CACM Research Highlights, Science, and the research blogs of IBM, Google, and Microsoft. He served as a program co chair for COLT 2016 and ALT 2021 conferences and as a co organizer of the Simons Institute Program on Data Privacy in 2019.
Host: Jon May and Thamme Gowda
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://youtu.be/_R8JFXvjnPcLocation: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://youtu.be/_R8JFXvjnPc
Audiences: NL Seminar
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
