Learning Natural Language Generation with Truncated Reinforcement Learning

1 code implementation • NAACL 2022 • Alice Martin, Guillaume Quispe, Charles Ollion, Sylvain Le Corff, Florian Strub, Olivier Pietquin

To our knowledge, it is the first approach that successfully learns a language generation policy without pre-training, using only reinforcement learning.

Language Modelling Question Generation +4

Diffusion bridges vector quantized Variational AutoEncoders

1 code implementation • 10 Feb 2022 • Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines

In this work, we propose a new model to train the prior and the encoder/decoder networks simultaneously.

Learning Natural Language Generation from Scratch

no code implementations • 20 Sep 2021 • Alice Martin Donati, Guillaume Quispe, Charles Ollion, Sylvain Le Corff, Florian Strub, Olivier Pietquin

This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original ap-proach to train conditional language models from scratch by only using reinforcement learning (RL).

Language Modelling reinforcement-learning +2