This paper surveys the current state of the art in Natural Language Generation (NLG),
defined as the task of generating text or speech from non-linguistic input. A survey of NLG is …

Dialogue systems have achieved growing success in many areas thanks to the rapid
advances of machine learning techniques. In the quest for generating more human-like …

Text style transfer is an important task in natural language generation, which aims to control
certain attributes in the generated text, such as politeness, emotion, humor, and many …

Natural language generation (NLG) is a critical component of spoken dialogue and it has a
significant impact both on usability and perceived quality. Most NLG systems in common use …

This paper introduces a neural model for concept-to-text generation that scales to large, rich
domains. We experiment with a new dataset of biographies from Wikipedia that is an order …

Most work on neural natural language generation (NNLG) focus on controlling the content of
the generated text. We experiment with controlling several stylistic aspects of the generated …

This paper provides a comprehensive analysis of the first shared task on End-to-End Natural
Language Generation (NLG) and identifies avenues for future research based on the results …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Moving from limited-domain natural language generation (NLG) to open domain is difficult
because the number of semantic input combinations grows exponentially with the number of …

This paper presents an empirical study of linguistic formality. We perform an analysis of
humans' perceptions of formality in four different genres. These findings are used to develop …