  Many problems related to the quality of requirements arise during elicitation
and specification activities since they are written in natural language. The
flexibility and inherent nature of language make requirements prone to
inconsistencies, redundancies, and ambiguities, and consequently, this
influences negatively the later phases of the software life cycle. To address
this problem, this paper proposes an innovative approach that combines natural
language processing techniques and recurrent neural networks to automatically
assess the quality of software requirements. Initially, the analysis of
singular, complete, correct, and appropriate quality properties defined in the
IEEE 29148: 2018 standard is addressed. The proposed neural models are trained
with a data set composed of 1000 software requirements. The proposal provides
an average accuracy of 75%. These promising results were a motivation to
explore its application in the evaluation of other quality properties
