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 …

Dialogue systems have attracted more and more attention. Recent advances on dialogue
systems are overwhelmingly contributed by deep learning techniques, which have been …

The majority of NLG evaluation relies on automatic metrics, such as BLEU. In this paper, we
motivate the need for novel, system-and data-independent automatic evaluation methods …

This paper describes the E2E data, a new dataset for training end-to-end, data-driven
natural language generation systems in the restaurant domain, which is ten times bigger …

We introduce GEM, a living benchmark for natural language Generation (NLG), its
Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving …

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 …

Semantically controlled neural response generation on limited-domain has achieved great
performance. However, moving towards multi-domain large-scale scenarios are shown to be …

This paper summarises the experimental setup and results of the first shared task on end-to-
end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to …

We model coherent conversation continuation via RNN-based dialogue models equipped
with a dynamic attention mechanism. Our attention-RNN language model dynamically …