An Empirical Investigation of Generalization and Transfer in Short Text Matching

Many tasks in natural language understanding, such as natural language inference, question answering, and paraphrasing can be viewed as short text matching problems. Recently, the emergence of a large number of datasets and deep learning models has made great success in short text matching. However, little study has been done on analyzing the generalization of these datasets across different text matching tasks, and how to leverage these supervised datasets of multiple domains to new domains to reduce the cost of annotating and improve their performance. In this paper, we conduct an extensive investigation of generalization and transfer across different datasets and show the factors that affect the generalization through visualization. Specially, we experiment with a conventional neural semantic matching model ESIM (enhanced sequential inference model) and a pre-trained language model BERT (bidirectional encoder representations from transformers) over 10 common datasets. We show that even BERT which is pre-trained on a large-scale dataset can still improve performance on the target dataset through transfer learning. Following our analysis, we also demonstrate that pre-training on multiple datasets shows good generalization and transfer. In the case of a new domain and few-shot setting, BERT which we pre-train on the multiple datasets first and then transfers to new datasets achieves exciting performance.