Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outputs and sometimes catastrophic safety issues. 
Prior studies on out-of-domain detection require in-domain task labels and are limited to supervised classification scenarios.
Our work tackles the problem of detecting out-of-domain samples with only unsupervised in-domain data. 
We utilize the latent representations of pre-trained transformers and propose a simple yet effective method to transform features across all layers to construct out-of-domain detectors efficiently. 
Two domain-specific fine-tuning approaches are further proposed to boost detection accuracy.
Our empirical evaluations of related methods on two datasets validate that our method greatly improves out-of-domain detection ability in a more general scenario.

Unsupervised Out-of-Domain Detection via Pre-trained Transformers

Poster 1H: Machine Learning for NLP

poster

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Keyang Xu

1

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Unsupervised Out-of-Domain Detection via Pre-trained Transformers

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