Search Results for author: Cyprien de Masson d'Autume

Found 12 papers, 6 papers with code

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

no code implementations NA 2021 Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent SIfre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d'Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, Geoffrey Irving

Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.

Fact Checking Language Modelling +4

Do Language Models Learn Commonsense Knowledge?

no code implementations31 Oct 2021 Xiang Lorraine Li, Adhiguna Kuncoro, Cyprien de Masson d'Autume, Phil Blunsom, Aida Nematzadeh

Language models (LMs) trained on large amounts of data have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup.

Multiple-choice

Adaptive Semiparametric Language Models

no code implementations4 Feb 2021 Dani Yogatama, Cyprien de Masson d'Autume, Lingpeng Kong

We present a language model that combines a large parametric neural network (i. e., a transformer) with a non-parametric episodic memory component in an integrated architecture.

Language Modelling

Mind the Gap: Assessing Temporal Generalization in Neural Language Models

1 code implementation NeurIPS 2021 Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d'Autume, Tomas Kocisky, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, Phil Blunsom

Hence, given the compilation of ever-larger language modelling datasets, combined with the growing list of language-model-based NLP applications that require up-to-date factual knowledge about the world, we argue that now is the right time to rethink the static way in which we currently train and evaluate our language models, and develop adaptive language models that can remain up-to-date with respect to our ever-changing and non-stationary world.

Language Modelling

A Mutual Information Maximization Perspective of Language Representation Learning

no code implementations ICLR 2020 Lingpeng Kong, Cyprien de Masson d'Autume, Wang Ling, Lei Yu, Zihang Dai, Dani Yogatama

We show state-of-the-art word representation learning methods maximize an objective function that is a lower bound on the mutual information between different parts of a word sequence (i. e., a sentence).

Natural Language Processing Representation Learning

Episodic Memory in Lifelong Language Learning

1 code implementation NeurIPS 2019 Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, Dani Yogatama

We introduce a lifelong language learning setup where a model needs to learn from a stream of text examples without any dataset identifier.

Continual Learning General Classification +2

Training language GANs from Scratch

5 code implementations NeurIPS 2019 Cyprien de Masson d'Autume, Mihaela Rosca, Jack Rae, Shakir Mohamed

Generative Adversarial Networks (GANs) enjoy great success at image generation, but have proven difficult to train in the domain of natural language.

Image Generation Text Generation

Learning and Evaluating General Linguistic Intelligence

no code implementations31 Jan 2019 Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor, Tomas Kocisky, Mike Chrzanowski, Lingpeng Kong, Angeliki Lazaridou, Wang Ling, Lei Yu, Chris Dyer, Phil Blunsom

We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to new tasks quickly.

Natural Language Understanding Question Answering

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