There has been an increasing interest in incorporating Artificial Intelligence (AI) into Defence and military systems to complement and augment human intelligence and capabilities. However, much work still needs to be done toward achieving an effective human-machine partnership. This work is aimed at enhancing human-machine communications by developing a capability for automatically translating human natural language into a machine-understandable language (e.g., SQL queries). Techniques toward achieving this goal typically involve building a semantic parser trained on a very large amount of high-quality manually-annotated data. However, in many real-world Defence scenarios, it is not feasible to obtain such a large amount of training data. To the best of our knowledge, there are few works trying to explore the possibility of training a semantic parser with limited manually-paraphrased data, in other words, zero-shot. In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.

翻译：随着将人工智能（AI）融入国防与军事系统以补充和增强人类智能及能力的兴趣日益增长，但实现有效的人机协同仍需大量工作。本研究旨在通过开发自动将人类自然语言转换为机器可理解语言（如SQL查询）的能力，来增强人机交互。实现该目标的技术通常涉及构建基于大规模高质量人工标注数据训练的语义解析器。然而在众多现实国防场景中，获取如此大规模的训练数据并不现实。据我们所知，目前鲜有研究探索利用有限人工释义数据（即零样本学习）训练语义解析器的可行性。本文研究了如何利用释义方法自动生成大规模训练数据集（以释义语句及其对应的SQL格式逻辑形式呈现），并展示了基于海事领域真实数据的实验结果。