Nemo：交互式数据编程的弱监督指导和情境化
Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming
摘要：
弱监督（WS）技术允许用户通过编程方式用启发式监督源标记数据来有效地创建大型训练数据集。虽然WS的成功在很大程度上依赖于所提供的标记策略，但在实践中如何创建这些策略的过程仍然未得到充分探索。在这项工作中，我们正式的开发过程中，标签的启发式的互动过程，围绕现有的工作流程，用户从一组选定的开发数据设计的启发式的来源。与形式主义，我们研究了两个核心问题，如何战略性地选择开发数据，以指导用户在有效地创建信息的统计，以及如何利用开发过程中的信息，以上下文和更好地学习结果的统计。建立在两种新的方法，有效地解决各自考虑的问题，我们提出了尼莫，一个端到端的交互式系统，提高了WS学习管道的整体生产力平均20%（和高达47%，在一个任务）相比，流行的WS方法。
英文摘要：
Weak Supervision (WS) techniques allow users to efficiently create large training datasets by programmatically labeling data with heuristic sources of supervision. While the success of WS relies heavily on the provided labeling heuristics, the process of how these heuristics are created in practice has remained under-explored. In this work, we formalize the development process of labeling heuristics as an interactive procedure, built around the existing workflow where users draw ideas from a selected set of development data for designing the heuristic sources. With the formalism, we study two core problems of how to strategically select the development data to guide users in efficiently creating informative heuristics, and how to exploit the information within the development process to contextualize and better learn from the resultant heuristics. Building upon two novel methodologies that effectively tackle the respective problems considered, we present Nemo, an end-to-end interactive system that improves the overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
