Learning Robust, Transferable Sentence Representations for Text Classification

Wasi Uddin Ahmad, Xueying Bai, Nanyun Peng, Kai-Wei Chang

Abstract: Despite deep recurrent neural networks (RNNs) demonstrate strong performance in text classification, training RNN models are often expensive and requires an extensive collection of annotated data which may not be available. To overcome the data limitation issue, existing approaches leverage either pre-trained word embedding or sentence representation to lift the burden of training RNNs from scratch. In this paper, we show that jointly learning sentence representations from multiple text classification tasks and combining them with pre-trained word-level and sentence level encoders result in robust sentence representations that are useful for transfer learning. Extensive experiments and analyses using a wide range of transfer and linguistic tasks endorse the effectiveness of our approach.

Keywords: sentence representations learning, multi-task learning, transfer learning