  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. This shared task aimed to assess whether recent
end-to-end NLG systems can generate more complex output by learning from
datasets containing higher lexical richness, syntactic complexity and diverse
discourse phenomena. Introducing novel automatic and human metrics, we compare
62 systems submitted by 17 institutions, covering a wide range of approaches,
including machine learning architectures -- with the majority implementing
sequence-to-sequence models (seq2seq) -- as well as systems based on
grammatical rules and templates. Seq2seq-based systems have demonstrated a
great potential for NLG in the challenge. We find that seq2seq systems
generally score high in terms of word-overlap metrics and human evaluations of
naturalness -- with the winning SLUG system (Juraska et al., 2018) being
seq2seq-based. However, vanilla seq2seq models often fail to correctly express
a given meaning representation if they lack a strong semantic control mechanism
applied during decoding. Moreover, seq2seq models can be outperformed by
hand-engineered systems in terms of overall quality, as well as complexity,
length and diversity of outputs. This research has influenced, inspired and
motivated a number of recent studies outwith the original competition, which we
also summarise as part of this paper.
