Automatic Synthesis of Diverse Weak Supervision Sources for Behavior
Analysis
- URL: http://arxiv.org/abs/2111.15186v1
- Date: Tue, 30 Nov 2021 07:51:12 GMT
- Title: Automatic Synthesis of Diverse Weak Supervision Sources for Behavior
Analysis
- Authors: Albert Tseng, Jennifer J. Sun, Yisong Yue
- Abstract summary: AutoSWAP is a framework for automatically synthesizing data-efficient task-level labeling functions.
We show that AutoSWAP is an effective way to automatically generate labeling functions that can significantly reduce expert effort for behavior analysis.
- Score: 37.077883083886114
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Obtaining annotations for large training sets is expensive, especially in
behavior analysis settings where domain knowledge is required for accurate
annotations. Weak supervision has been studied to reduce annotation costs by
using weak labels from task-level labeling functions to augment ground truth
labels. However, domain experts are still needed to hand-craft labeling
functions for every studied task. To reduce expert effort, we present AutoSWAP:
a framework for automatically synthesizing data-efficient task-level labeling
functions. The key to our approach is to efficiently represent expert knowledge
in a reusable domain specific language and domain-level labeling functions,
with which we use state-of-the-art program synthesis techniques and a small
labeled dataset to generate labeling functions. Additionally, we propose a
novel structural diversity cost that allows for direct synthesis of diverse
sets of labeling functions with minimal overhead, further improving labeling
function data efficiency. We evaluate AutoSWAP in three behavior analysis
domains and demonstrate that AutoSWAP outperforms existing approaches using
only a fraction of the data. Our results suggest that AutoSWAP is an effective
way to automatically generate labeling functions that can significantly reduce
expert effort for behavior analysis.
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