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  • NL Seminar- Fantastic Continuous-valued Sentence Representations and How to Find Them

    Thu, Jul 29, 2021 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars

    Speaker: Nishant Subramani, Allen NLP

    Talk Title: Fantastic Continuous-valued Sentence Representations and How to Find Them

    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 admin2 AT isi.edu beforehand so we'll be aware of your attendance and let you in.

    Recently, large pretrained language models have seen tremendous success in few-shot and zero shot learning settings when given appropriate prompting. For these models to excel in this few shot paradigm, the provided prompts must condition the language model to produce the desired output sequence. We investigate a more general property of this idea: does there exist a continuous, fixed length vector that can prompt a pretrained and frozen language model to generate any output sentence of interest? To answer this, we develop a language model agnostic sentence representation discovery algorithm, which learns a continuous-valued, fixed-length vector for a sequence by adding the vector at various locations in the language model and optimizing it to maximize the likelihood of the sequence. Experiments reveal that for nearly all English sentences, greater than 98 percent from different genres and corpora, we can find a vector that recovers our sentence of interest exactly without fine tuning the underlying language model. In addition, we find that our representations can be learned in real-time and are robust to initialization; convergence requires less than 20 iterations on average using stochastic gradient descent with Adam.

    Biography: Nishant is a predoctoral young investigator on the AllenNLP team at AI2 working with Matt Peters and part of the Masakhane community. He is broadly interested in sentence representation, ML for social good, and out-of-distribution generalization research. Previously, Nishant has spent time in industry working on document analysis, OCR, fake speech detection, and audio-driven facial animation. He also has worked with Kyunghyun Cho, Sam Bowman, and Doug Downey during his time at NYU and Northwestern on NLP and causality research.

    Host: Jon May and Mozhdeh Gheini

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

    Webcast: https://youtu.be/pCKBSPDenpc

    Location: Information Science Institute (ISI) - Virtual Only

    WebCast Link: https://youtu.be/pCKBSPDenpc

    Audiences: Everyone Is Invited

    Contact: Petet Zamar

    Event Link: https://nlg.isi.edu/nl-seminar/


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