With the rapid development of the Internet, the massive amount of web textual data has
grown exponentially, which has brought considerable challenges to downstream tasks, such …

For many new application domains for data-to-text generation, the main obstacle in training
neural models consists of a lack of training data. While usually large numbers of instances …

Recent advancements in data-to-text generation largely take on the form of neural end-to-
end systems. Efforts have been dedicated to improving text generation systems by changing …

Neural natural language generation (NLG) and understanding (NLU) models are data-
hungry and require massive amounts of annotated data to be competitive. Recent …

Generating an informative and attractive title for the product is a crucial task for e-commerce.
Most existing works follow the standard multimodal natural language generation …

Using computers to replace pilot seats in air traffic control (ATC) simulators is an effective
way to improve controller training efficiency and reduce training costs. To achieve this, we …

West African Pidgin English is a language that is significantly spoken in West Africa,
consisting of at least 75 million speakers. Nevertheless, proper machine translation systems …

There have been many studies on neural headline generation models trained with a lot of
(article, headline) pairs. However, there are few situations for putting such models into …

Text generation aims to produce human-like natural language output for down-stream tasks.
It covers a wide range of applications like machine translation, document summarization …

We present a recurrent neural network based system for automatic quality estimation of
natural language generation (NLG) outputs, which jointly learns to assign numerical ratings …