Attention has arguably become one of the most important concepts in the deep learning
field. It is inspired by the biological systems of humans that tend to focus on the distinctive …

Attention is an increasingly popular mechanism used in a wide range of neural
architectures. The mechanism itself has been realized in a variety of formats. However …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous
improvements across various NLP tasks, and its consecutive variants have been proposed …

Neural network models have been widely used in the field of natural language processing
(NLP). Recurrent neural networks (RNNs), which have the ability to process sequences of …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Current end-to-end machine reading and question answering (Q\&A) models are primarily
based on recurrent neural networks (RNNs) with attention. Despite their success, these …

Text classification is a critical research topic with broad applications in natural language
processing. Recently, graph neural networks (GNNs) have received increasing attention in …

Stock price modeling and prediction have been challenging objectives for researchers and
speculators because of noisy and non-stationary characteristics of samples. With the growth …

In this paper, we present the gated self-matching networks for reading comprehension style
question answering, which aims to answer questions from a given passage. We first match …