This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …

Statistical language models have recently been successfully applied to many information
retrieval problems. A great deal of recent work has shown that statistical language models …

Pretraining large neural language models, such as BERT, has led to impressive gains on
many natural language processing (NLP) tasks. However, most pretraining efforts focus on …

During real-time language comprehension, our minds rapidly decode complex meanings
from sequences of words. The difficulty of doing so is known to be related to words' …

We propose a simple data augmentation protocol aimed at providing a compositional
inductive bias in conditional and unconditional sequence models. Under this protocol …

How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

Language identification (" LI") is the problem of determining the natural language that a
document or part thereof is written in. Automatic LI has been extensively researched for over …

Vector‐based models of word meaning have become increasingly popular in cognitive
science. The appeal of these models lies in their ability to represent meaning simply by …

We survey the most widely-used algorithms for smoothing models for language n-gram
modeling. We then present an extensive empirical comparison of several of these smoothing …

Abstract Domain-specific internet portals are growing in popularity because they gather
content from the Web and organize it for easy access, retrieval and search. For example …