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

Over the last several years, the field of natural language processing has been propelled
forward by an explosion in the use of deep learning models. This article provides a brief …

In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning
representations across multiple natural language understanding (NLU) tasks. MT-DNN not …

Existing question answering (QA) datasets fail to train QA systems to perform complex
reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset …

Inductive transfer learning has greatly impacted computer vision, but existing approaches in
NLP still require task-specific modifications and training from scratch. We propose Universal …

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 …

Computational linguistics has enabled the introduction of objective tools that measure some
of the symptoms of schizophrenia, including the coherence of speech associated with formal …

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

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 …

Deep learning has improved performance on many natural language processing (NLP)
tasks individually. However, general NLP models cannot emerge within a paradigm that …