Papers › Keep Learning: Self-supervised Meta-learning for Learning from Inference

Keep Learning: Self-supervised Meta-learning for Learning from Inference

1 Apr 2021EACL 2021 2archive 2025-07-28

Akhil Kedia, Sai Chetan Chinthakindi

A common approach in many machine learning algorithms involves self-supervised learning on large unlabeled data before fine-tuning on downstream tasks to further improve performance. A new approach for language modelling, called dynamic evaluation, further fine-tunes a trained model during inference using trivially-present ground-truth labels, giving a large improvement in performance. However, this approach does not easily extend to classification tasks, where ground-truth labels are absent during inference. We propose to solve this issue by utilizing self-training and back-propagating the loss from the model{'}s own class-balanced predictions (pseudo-labels), adapting the Reptile algorithm from meta-learning, combined with an inductive bias towards pre-trained weights to improve generalization. Our method improves the performance of standard backbones such as BERT, Electra, and ResNet-50 on a wide variety of tasks, such as question answering on SQuAD and NewsQA, benchmark task SuperGLUE, conversation response selection on Ubuntu Dialog corpus v2.0, as well as image classification on MNIST and ImageNet without any changes to the underlying models. Our proposed method outperforms previous approaches, enables self-supervised fine-tuning during inference of any classifier model to better adapt to target domains, can be easily adapted to any model, and is also effective in online and transfer-learning settings.

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Tasks

Answer Selection Image Classification Inductive Bias Language Modelling Meta-Learning Question Answering Self-Supervised Learning Transfer Learning image-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Answer Selection	Ubuntu Dialogue (v2, Ranking)	BERT + Keep Learning	1 in 10 R@1	82.4	#1 of 2	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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