Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

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 …

It is well known that the standard likelihood training and approximate decoding objectives in
neural text generation models lead to less human-like responses for open-ended tasks such …

Temporal knowledge graph, serving as an effective way to store and model dynamic
relations, shows promising prospects in event forecasting. However, most temporal …

Bidirectional Encoder Representations from Transformers (BERT) represents the latest
incarnation of pretrained language models which have recently advanced a wide range of …

Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been
investigated intensively in recent years due to their ability to model and predict nonlinear …

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 …

Conversational recommender systems (CRS) aim to recommend high-quality items to users
through interactive conversations. Although several efforts have been made for CRS, two …

Humans gather information through conversations involving a series of interconnected
questions and answers. For machines to assist in information gathering, it is therefore …