Recent advances in arti cial intelligence (AI) o er an opportunity for the adoption of self-driving networks. However, network operators or home-network users still do not have the right tools to exploit these new advancements in AI, since they have to rely on low-level languages to specify network policies. Intent-based networking (IBN) allows operators to specify high-level policies that dictate how the network should behave without worrying how they are translated into con guration commands in the network devices. However, the existing research proposals for IBN fail to exploit the knowledge and feedback from the network operator to validate or improve the translation of intents. In this paper, we introduce a novel intent-re nement process that uses machine learning and feedback from the operator to translate the operator's u erances into network con gurations. Our re nement process uses a sequence-to-sequence learning model to extract intents from natural language and the feedback from the operator to improve learning. e key insight of our process is an intermediate representation that resembles natural language that is suitable to collect feedback from the operator but is structured enough to facilitate precise translations. Our prototype interacts with a network operator using natural language and translates the operator input to the intermediate representation before translating to SDN rules. Our experimental results show that our process achieves a correlation coe cient squared (i.e., R-squared) of 0.99 for a dataset with 5000 entries and the operator feedback signi cantly improves the accuracy of our model.

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