Extracting Salient Facts from Company Reviews with Scarce Labels

In this paper, we propose the task of extracting salient facts from online company reviews. Salient facts present unique and distinctive information about a company, which helps the user in deciding whether to apply to the company. We formulate the salient fact extraction task as a text classification problem, and leverage pretrained language models to tackle the problem. However, the scarcity of salient facts in company reviews causes a serious label imbalance issue, which hinders taking full advantage of pretrained language models. To address the issue, we developed two data enrichment methods: first, representation enrichment, which highlights uncommon tokens by appending special tokens, and second, label propagation, which interactively creates pseudopositive examples from unlabeled data. Experimental results on an online company review corpus show that our approach improves the performance of pretrained language models by up to an F1 score of 0.24. We also confirm that our approach competitively performs well against the state-of-the-art data augmentation method on the SemEval 2019 benchmark even when trained with only 20% of training data.