Abstract
Text generation from semantic parses is to generate textual descriptions for the formal representation inputs such as logic forms and SQL queries. This is challenging for two reasons: (1) the complex and intensive inner logic with data scarcity constraint, (2) the lack of automatic logic consistency evaluation metrics. To address these two challenges, this paper first proposes SNOWBALL, a framework for text generation from semantic parses that employs an iterative training procedure by recursively augmenting the training set with quality control. Second, we propose a novel automatic metric, BLEC, for evaluating the logical consistency between the semantic parses and generated texts. The experimental results on two benchmark datasets, Logic2Text and Spider, demonstrate the SNOWBALL framework enhances the logic consistency on both BLEC and human evaluation. Furthermore, our statistical analysis reveals that BLEC is more logically consistent with human evaluation than general-purpose automatic metrics including BLEU, ROUGE and BLEURT.
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