LAUSR

Supervised machine learning methods depend highly on the quality of the training dataset and the underlying model. In particular, neural network models, that have shown great success in dealing with natural language problems, require a large dataset to learn a vast number of parameters. However, it is not always easy to build a large (labelled) dataset. For example, due to the complex nature of tweets and the manual labour involved, it is hard to create a large Twitter data set with the misogynistic label. In this paper, we propose to regularise a long short-term memory (LSTM) classifier using a pretrained LSTM-based language model (LM) to build an accurate classification model with a small training set. We explain transfer learning (TL) with a Bayesian interpretation and show that TL can be viewed as an uncertainty regularisation technique in Bayesian inference. We show that a LM pre-trained on a sequence of general to task-specific domain datasets can be used to regularise a LSTM classifier effectively when a small training dataset is available. Empirical analysis with two small Twitter datasets reveals that an LSTM model trained in this way can outperform the state-of-the-art classification models.