  Neural natural language generation (NNLG) from structured meaning
representations has become increasingly popular in recent years. While we have
seen progress with generating syntactically correct utterances that preserve
semantics, various shortcomings of NNLG systems are clear: new tasks require
new training data which is not available or straightforward to acquire, and
model outputs are simple and may be dull and repetitive. This paper addresses
these two critical challenges in NNLG by: (1) scalably (and at no cost)
creating training datasets of parallel meaning representations and reference
texts with rich style markup by using data from freely available and naturally
descriptive user reviews, and (2) systematically exploring how the style markup
enables joint control of semantic and stylistic aspects of neural model output.
We present YelpNLG, a corpus of 300,000 rich, parallel meaning representations
and highly stylistically varied reference texts spanning different restaurant
attributes, and describe a novel methodology that can be scalably reused to
generate NLG datasets for other domains. The experiments show that the models
control important aspects, including lexical choice of adjectives, output
length, and sentiment, allowing the models to successfully hit multiple style
targets without sacrificing semantics.

Computation and Language