Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming

• URL: http://arxiv.org/abs/2203.01382v1
• Date: Wed, 2 Mar 2022 19:57:32 GMT
• Title: Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming
• Authors: Cheng-Yu Hsieh, Jieyu Zhang, Alexander Ratner
• Abstract summary: We present Nemo, an end-to-end interactive Supervision system that improves overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS supervision approach.
• Score: 77.38174112525168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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