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

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Manage episode 352483018 series 1452120
Kandungan disediakan oleh NLP Highlights and Allen Institute for Artificial Intelligence. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh NLP Highlights and Allen Institute for Artificial Intelligence atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
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 models with non-parametric distributions over the closest values in some data stores built from relevant data. Not only are these models shown to outperform the usual parametric language models, they also have important implications on memorization and generalization in language models. Urvashi's webpage: https://urvashik.github.io Papers discussed: 1) Generalization through memorization: Nearest Neighbor Language Models (https://www.semanticscholar.org/paper/7be8c119dbe065c52125ee7716601751f3116844) 2)Nearest Neighbor Machine Translation (https://www.semanticscholar.org/paper/20d51f8e449b59c7e140f7a7eec9ab4d4d6f80ea)
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145 episod

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Manage episode 352483018 series 1452120
Kandungan disediakan oleh NLP Highlights and Allen Institute for Artificial Intelligence. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh NLP Highlights and Allen Institute for Artificial Intelligence atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
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 models with non-parametric distributions over the closest values in some data stores built from relevant data. Not only are these models shown to outperform the usual parametric language models, they also have important implications on memorization and generalization in language models. Urvashi's webpage: https://urvashik.github.io Papers discussed: 1) Generalization through memorization: Nearest Neighbor Language Models (https://www.semanticscholar.org/paper/7be8c119dbe065c52125ee7716601751f3116844) 2)Nearest Neighbor Machine Translation (https://www.semanticscholar.org/paper/20d51f8e449b59c7e140f7a7eec9ab4d4d6f80ea)
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145 episod

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