A Multi-task Approach to Open Domain Suggestion Mining Using Language Model for Text Over-Sampling

Consumer reviews online may contain suggestions useful for improving commercial products and services. Mining suggestions is challenging due to the absence of large labeled and balanced datasets. Furthermore, most prior studies attempting to mine suggestions, have focused on a single domain such as Hotel or Travel only. In this work, we introduce a novel over-sampling technique to address the problem of class imbalance, and propose a multi-task deep learning approach for mining suggestions from multiple domains. Experimental results on a publicly available dataset show that our over-sampling technique, coupled with the multi-task framework outperforms state-of-the-art open domain suggestion mining models in terms of the F-1 measure and AUC.