ICLR
|
2022

Creating Training Sets via Weak Indirect Supervision

J. Zhang, et al

Abstract

This paper extends the scope of usable sources in WS, by formulating Weak Indirect Supervision (WIS), a new research problem for automatically synthesizing training labels based on indirect supervision sources that have different output label spaces.

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