NLP Highlights30 Mars 2020

109 - What Does Your Model Know About Language, with Ellie Pavlick

How do we know, in a concrete quantitative sense, what a deep learning model knows about language? In this episode, Ellie Pavlick talks about two broad directions to address this question: structural and behavioral analysis of models. In structural analysis, we often train a linear classifier for some linguistic phenomenon we'd like to probe (e.g., syntactic dependencies) while using the (frozen) weights of a model pre-trained on some tasks (e.g., masked language models). What can we conclude from the results of probing experiments? What does probing tell us about the linguistic abstractions encoded in each layer of an end-to-end pre-trained model? How well does it match classical NLP pipelines? How important is it to freeze the pre-trained weights in probing experiments? In contrast, behavioral analysis evaluates a model's ability to distinguish between inputs which respect vs. violate a linguistic phenomenon using acceptability or entailment tasks, e.g., can the model predict which is more likely: "dog bites man" vs. "man bites dog"? We discuss the significance of which format to use for behavioral tasks, and how easy it is for humans to perform such tasks. Ellie Pavlick's homepage: https://cs.brown.edu/people/epavlick/ BERT rediscovers the classical nlp pipeline , by Ian Tenney, Dipanjan Das, Ellie Pavlick https://arxiv.org/pdf/1905.05950.pdf?fbclid=IwAR3gzFibSBoDGdjqVu9Gq0mh1lDdRZa7dm42JuXXUfjG6rKZ44iHIOdV6jg Inherent Disagreements in Human Textual Inferences by Ellie Pavlick and Tom Kwiatkowski https://www.mitpressjournals.org/doi/full/10.1162/tacl_a_00293

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Avsnitt(145)

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141 - Building an open source LM, with Iz Beltagy and Dirk Groeneveld

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140 - Generative AI and Copyright, with Chris Callison-Burch

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139 - Coherent Long Story Generation, with Kevin Yang

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24 Mars 202345min

138 - Compositional Generalization in Neural Networks, with Najoung Kim

Compositional generalization refers to the capability of models to generalize to out-of-distribution instances by composing information obtained from the training data. In this episode we chatted with...

20 Jan 202348min

137 - Nearest Neighbor Language Modeling and Machine Translation, with Urvashi Khandelwal

We invited Urvashi Khandelwal, a research scientist at Google Brain to talk about nearest neighbor language and machine translation models. These models interpolate parametric (conditional) language m...

13 Jan 202335min