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 …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Early studies mainly focused on answering simple questions over KBs and …

In recent years, considerable research has been dedicated to the application of neural
models in the field of natural language generation (NLG). The primary objective is to …

The reviews written by users in E-commerce platform have been fully exploited by product-
related question answering systems, which ignore the product descriptions with valuable …

Recent advances in neural network-based generative modeling have reignited the hopes in
having computer systems capable of seamlessly conversing with humans and able to …

Commonsense and background knowledge is required for a QA model to answer many
nontrivial questions. Different from existing work on knowledge-aware QA, we focus on a …

Question answering (QA) is an important aspect of open-domain conversational agents,
garnering specific research focus in the conversational QA (ConvQA) subtask. One notable …

Recent advances in deep neural language models combined with the capacity of large
scale datasets have accelerated the development of natural language generation systems …

The use of virtual agents (bots) has become essential for providing online assistance to
customers. However, even though a lot of effort has been dedicated to the research …

Learning to control the structure of sentences is a challenging problem in text generation.
Existing work either relies on simple deterministic approaches or RL-based hard structures …