论文标题
网络配置的神经语言模型：机会和现实检查
Neural language models for network configuration: Opportunities and reality check
论文作者
论文摘要
自然语言处理（NLP）技术的增强，最近已经看到了壮观的进步，这主要是由具有单词嵌入（例如Word2Vec）以及新颖的体系结构（例如，变形金刚）的代表学习中的突破所推动的。这一成功迅速邀请研究人员探索NLP技术对其他字段（例如计算机编程语言）的使用，并有望在软件编程中自动化任务（错误检测，代码合成，代码修复，跨语言翻译等）。通过扩展，NLP也有可能应用于网络配置语言，例如考虑诸如网络配置验证，综合和跨供应商翻译等任务。在本文中，我们调查了针对编程语言的最新进展，目的是为了验证，综合和翻译：尤其是，我们尤其是，我们回顾了他们的培训要求和预期的性能，并定性评估类似技术是否可以使网络中相应的用例受益。
Boosted by deep learning, natural language processing (NLP) techniques have recently seen spectacular progress, mainly fueled by breakthroughs both in representation learning with word embeddings (e.g. word2vec) as well as novel architectures (e.g. transformers). This success quickly invited researchers to explore the use of NLP techniques to other fields, such as computer programming languages, with the promise to automate tasks in software programming (bug detection, code synthesis, code repair, cross language translation etc.). By extension, NLP has potential for application to network configuration languages as well, for instance considering tasks such as network configuration verification, synthesis, and cross-vendor translation. In this paper, we survey recent advances in deep learning applied to programming languages, for the purpose of code verification, synthesis and translation: in particularly, we review their training requirements and expected performance, and qualitatively assess whether similar techniques can benefit corresponding use-cases in networking.
