Recent advances in corpus-based Natural Language Generation (NLG) hold the
promise of being easily portable across domains, but require costly training
data, consisting of meaning representations (MRs) paired with Natural Language
(NL) utterances. In this work, we propose a novel framework for crowdsourcing
high quality NLG training data, using automatic quality control measures and
evaluating different MRs with which to elicit data. We show that pictorial MRs
result in better NL data being collected than logic-based MRs: utterances
elicited by pictorial MRs are judged as significantly more natural, more
informative, and better phrased, with a significant increase in average quality
ratings (around 0.5 points on a 6-point scale), compared to using the logical
MRs. As the MR becomes more complex, the benefits of pictorial stimuli
increase. The collected data will be released as part of this submission.

本文提出一种新的框架，使用自动质量控制措施并评估不同 MR 来引出数据，从而通过众包获得高质量的 NLG 训练数据。研究表明，与逻辑 MR 相比，使用图像 MR 可以收集到更好的数据，这些数据被评为更自然、更信息丰富、更好地措辞，平均质量评分显著提高。