Evaluating Human-Machine Translation with Attention Mechanisms for Industry 4.0 Environment SQL-Based Systems

The use of relational databases is increasingly present in the industry. Applications in medical, IoT and industry 4.0 are examples of this. Despite the large capacity and efficiency in storing and retrieving data, this type of database requires technical knowledge in specific query languages to access this information, which distances these types of application from the lay public. In this work, we propose an application of recent models in natural language processing that use neural networks, as well as attention mechanisms for the translation of natural language in English to SQL applied in an SQL database of a system to store data from sensors, focused on the concept of Industry 4.0. Paired examples of natural language phrases were generated with their corresponding SQL query to be used for training and validation. By training the neural network, we obtained a language model with an accuracy of approximately 99 % in the validation set.