Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

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 …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

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 …

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 …

Deep learning methods employ multiple processing layers to learn hierarchical
representations of data, and have produced state-of-the-art results in many domains …

Standard accuracy metrics indicate that reading comprehension systems are making rapid
progress, but the extent to which these systems truly understand language remains unclear …

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 …

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast
the task as a multi-turn question answering problem, ie, the extraction of entities and …

Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-
to-end deep neural networks. Specialized neural models have been developed for …