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2021 EMNLP EMNLP 2021

ODIST: Open World Classification via Distributionally Shifted Instances

Abstract

AbstractIn this work, we address the open-world classification problem with a method called ODIST, open world classification via distributionally shifted instances. This novel and straightforward method can create out-of-domain instances from the in-domain training instances with the help of a pre-trained generative language model. Experimental results show that ODIST performs better than state-of-the-art decision boundary finding method.

🐣 Hot Topic Early Bird — generative language model
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Lei Shu , Yassine Benajiba , Saab Mansour , Yi Zhang

Topics

Machine Learning > Application Areas > Domain Adaptation Machine Learning > Learning Types > Out-of-Distribution Detection Machine Learning > Learning Types > Distribution Shift

Keywords

domain adaptation text classification distribution shift generative model language model out-of-distribution detection generative language model open-world classification
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