CRM

The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 […]

We introduce the Dynamic Coattention Network, a state of the art neural network designed to automatically answer questions about documents. Instead of producing a single, static representation of the document without context, our system is able to interpret the document differently depending on the question. That is, given the same document, the system constructs a

Humans learn natural languages, such as English, starting from basic grammar to complex semantics in a single brain. How do we build such a single model to handle a variety of natural language processing (NLP) tasks in computers? As a first step towards this goal, we have developed a single deep neural network model which

LSTMs have become a basic building block for many deep NLP models. In recent years, many improvements and variations have been proposed for deep sequence models in general, and LSTMs in particular. We propose and analyze a series of augmentations and modifications to LSTM networks resulting in improved performance for text classification datasets. We observe

Automatically generating captions for images has emerged as a prominent interdisciplinary research problem in both academia and industry. It can aid visually impaired users, and make it easy for users to organize and navigate through large amounts of typically unstructured visual data. In order to generate high quality captions, the model needs to incorporate fine-grained