Come dive into one of the curiously delightful conversations overheard at National Geographic’s headquarters, as we follow explorers, photographers, and scientists to the edges of our big, weird, beautiful world. Hosted by Peter Gwin and Amy Briggs.
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109 - What Does Your Model Know About Language, with Ellie Pavlick
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Manage episode 257394627 series 1452120
Innehåll tillhandahållet av NLP Highlights and Allen Institute for Artificial Intelligence. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av NLP Highlights and Allen Institute for Artificial Intelligence eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
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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145 episoder
MP3•Episod hem
Manage episode 257394627 series 1452120
Innehåll tillhandahållet av NLP Highlights and Allen Institute for Artificial Intelligence. Allt poddinnehåll inklusive avsnitt, grafik och podcastbeskrivningar laddas upp och tillhandahålls direkt av NLP Highlights and Allen Institute for Artificial Intelligence eller deras podcastplattformspartner. Om du tror att någon använder ditt upphovsrättsskyddade verk utan din tillåtelse kan du följa processen som beskrivs här https://sv.player.fm/legal.
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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145 episoder
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