Grid Structure Attention for Natural Language Interface to Bash Commands

Natural language interfaces are user interfaces that allow a human to interact with the computer by using natural languages. Mapping natural languages to bash commands is a novel interface problem and has been attracted great curiosity in recent years. Due to application domains of bash commands including the file system administration, the networking management, the file processing, etc, we need novel mechanisms of parameterization and normalization on this rich domain. In this paper, we propose a new mechanism, called GSAM (Grid Structure Attention Mechanism). This mechanism is used in the recurrent neural networks (RNNs) base in the sequence-to-sequence model. Our mechanism first uses a non-linear function to parameterize natural language sentences, and then use another model to capture the adjacency information of the natural language sentences. To show the robustness of GSAM, we reduce the training dataset of utility bash commands and GSAM still gets better performance in Template Matching scores and BLEU-scores.