1.6k citations · 2.3k across the 6 of their papers we have counts for
12 papers · 1 filter
DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
Baptiste Roziere, Marie-Anne Lachaux, Marc Szafraniec +1
Recent advances in self-supervised learning have dramatically improved the state of the art on a wide variety of tasks. However, research in language model pre-training has mostly…
Unsupervised Translation of Programming Languages
Marie-Anne Lachaux, Baptiste Roziere, Lowik Chanussot +1
A transcompiler, also known as source-to-source translator, is a system that converts source code from a high-level programming language (such as C++ or Python) to another. Transco…
Large Memory Layers with Product Keys
Guillaume Lample, Alexandre Sablayrolles, Marc'Aurelio Ranzato +2
This paper introduces a structured memory which can be easily integrated into a neural network. The memory is very large by design and significantly increases the capacity of the a…
The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English
Francisco Guzmán, Peng-Jen Chen, Myle Ott +5
For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challeng…
Cross-lingual Language Model Pretraining
Guillaume Lample, Alexis Conneau
Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages an…
Multiple-Attribute Text Style Transfer
Sandeep Subramanian, Guillaume Lample, Eric Michael Smith +3
The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "styl…