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.

通过自然描述性用户评论数据免费生成语义和样式标记富文本的可并行结构化意义表示的训练数据集，系统地探索样式标记如何实现神经模型输出的语义和样式联合控制，提出了 YelpNLG，一个跨越不同餐厅属性的高度风格多变的参考文本和富含语义的并行结构化意义表示语料库，实验结果表明该模型可以在保持语义的前提下，成功地达到多个样式目标，包括形容词的词汇选择，输出长度和情感。