Abstract
Fine-tuning pre-trained sentence embedding models like BERT has become the default transfer learning approach for several NLP tasks like text classification. We propose an alternative transfer learning approach called SimpleTran which is simple and effective for low resource text classification characterized by small sized datasets. We train a simple sentence embedding model on the target dataset, combine its output embedding with that of the pre-trained model via concatenation or dimension reduction, and finally train a classifier on the combined embedding either by fixing the embedding model weights or training the classifier and the embedding models end-to-end. Keeping embeddings fixed, SimpleTran significantly improves over fine-tuning on small datasets, with better computational efficiency. With end-to-end training, SimpleTran outperforms fine-tuning on small and medium sized datasets with negligible computational overhead. We provide theoretical analysis for our method, identifying conditions under which it has advantages.
Abstract (translated)
URL
https://arxiv.org/abs/2004.05119
