This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …

With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …

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 …

The task of named entity recognition (NER) is normally divided into nested NER and flat
NER depending on whether named entities are nested or not. Models are usually separately …

Attention mechanisms have seen wide adoption in neural NLP models. In addition to
improving predictive performance, these are often touted as affording transparency: models …

Studies have shown that a dominant class of questions asked by visually impaired users on
images of their surroundings involves reading text in the image. But today's VQA models can …

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 …

We introduce a novel network, called as CO-attention Siamese Network (COSNet), to
address the unsupervised video object segmentation task from a holistic view. We …

This paper studies the problem of learning semantic segmentation from image-level
supervision only. Current popular solutions leverage object localization maps from …

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 …