Olivier Salaün, Frédéric Piedboeuf, Guillaume Le Berre, David Alfonso-Hermelo, Philippe Langlais. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

We propose in this work a new unsupervised training procedure that is most effective when it is applied after supervised training and fine-tuning of deep neural network classifiers. While standard regularization techniques combat overfitting by means that are unrelated to the target classification loss, such as by minimizing the L2 norm or by adding noise either in the data, model or process, the proposed unsupervised training loss reduces overfitting by optimizing the true classifier risk. The proposed approach is evaluated on several tasks of increasing difficulty and varying conditions: unsupervised training, post-tuning and anomaly detection. It is also tested both on simple neural networks, such as small multi-layer perceptron, and complex Natural Language Processing models, e.g., pretrained BERT embeddings. Experimental results confirm the theory and show that the proposed approach gives the best results in post-tuning conditions, i.e., when applied after supervised training and fine-tuning.