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

This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

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 …

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 …

The latest work on language representations carefully integrates contextualized features into
language model training, which enables a series of success especially in various machine …

We study knowledge-grounded dialogue generation with pre-trained language models. To
leverage the redundant external knowledge under capacity constraint, we propose …

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

Edge features contain important information about graphs. However, current state-of-the-art
neural network models designed for graph learning, eg, graph convolutional networks …

In this paper, we present a fast and strong neural approach for general purpose text
matching applications. We explore what is sufficient to build a fast and well-performed text …

In recent times, sequence-to-sequence (seq2seq) models have gained a lot of popularity
and provide state-of-the-art performance in a wide variety of tasks, such as machine …