In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …

Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …

Over the last few years, large language models (LLMs) have emerged as the most important
breakthroughs in natural language processing (NLP) that fundamentally transform research …

Despite rapid progress in the recent past, current speech recognition systems still require
labeled training data which limits this technology to a small fraction of the languages spoken …

This paper demonstrates that multilingual denoising pre-training produces significant
performance gains across a wide variety of machine translation (MT) tasks. We present …

Much recent progress in applications of machine learning models to NLP has been driven
by benchmarks that evaluate models across a wide variety of tasks. However, these broad …

Recent studies have demonstrated the efficiency of generative pretraining for English
natural language understanding. In this work, we extend this approach to multiple …

We study how neural networks trained by gradient descent extrapolate, ie, what they learn
outside the support of the training distribution. Previous works report mixed empirical results …

'Vital reading. This is the book on artificial intelligence we need right now.'Mike Krieger,
cofounder of Instagram Artificial intelligence is rapidly dominating every aspect of our …

This paper shows that pretraining multilingual language models at scale leads to significant
performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer …