  In order to reduce the manual cost of designing ER models, recent approaches
have been proposed to address the task of NL2ERM, i.e., automatically
generating entity-relationship (ER) models from natural language (NL)
utterances such as software requirements. These approaches are typically
rule-based ones, which rely on rigid heuristic rules; these approaches cannot
generalize well to various linguistic ways of describing the same requirement.
Despite having better generalization capability than rule-based approaches,
deep-learning-based models are lacking for NL2ERM due to lacking a large-scale
dataset. To address this issue, in this paper, we report our insight that there
exists a high similarity between the task of NL2ERM and the increasingly
popular task of text-to-SQL, and propose a data transformation algorithm that
transforms the existing data of text-to-SQL into the data of NL2ERM. We apply
our data transformation algorithm on Spider, one of the most popular
text-to-SQL datasets, and we also collect some data entries with different NL
types, to obtain a large-scale NL2ERM dataset. Because NL2ERM can be seen as a
special information extraction (IE) task, we train two state-of-the-art IE
models on our dataset. The experimental results show that both the two models
achieve high performance and outperform existing baselines.
