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 …

Responses in task-oriented dialogue systems often realize multiple propositions whose
ultimate form depends on the use of sentence planning and discourse structuring …

Neural generation methods for task-oriented dialogue typically generate from a meaning
representation that is populated using a database of domain information, such as a table of …

Prompt-based or in-context learning has achieved high zero-shot performance on many
natural language generation (NLG) tasks. Here we explore the performance of prompt …

We present a review of personality in neural conversational agents (CAs), also called
chatbots. First, we define Personality, Persona, and Profile. We explain all personality …

We present a comparison of word-based and character-based sequence-to-sequence
models for data-to-text natural language generation, which generate natural language …

One challenge with open-domain dialogue systems is the need to produce truthful, high-
quality responses on any topic. We aim to improve the quality and coverage of Athena, an …

Users attribute artificial personality (AP) to conversational agents (CAs) based on perceived
language respectively verbal cues. This review synthesizes studies on this topic …

In recent years we have witnessed a surge in machine learning methods that provide
machines with conversational abilities. Most notably, neural-network–based systems have …

Traditional statistical natural language generation (NLG) systems require substantial hand-
engineering: many of the components, such as content planners, sentence planners and …