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Incidental Supervision: Moving beyond Supervised Learning by Roth, Dan is a scholarly article available to read on EtoBox.

What is Incidental Supervision: Moving beyond Supervised Learning about?

Machine Learning and Inference methods have become ubiquitous in our attempt to induce more abstract representations of natural language text, visual scenes, and other messy, naturally occurring data, and support decisions that depend on it. However, learning models for these tasks is difficult partly because generating the necessary supervision signals for it is costly and does not scale. This paper describes several learning paradigms that are designed to alleviate the supervision bottleneck. It will illustrate their benefit in the context of multiple problems, all pertaining to inducing various levels of semantic representations from text.

Author

Roth, Dan

Published

2020

Language

EN