id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020794-d3oru1w5	Leekha, Maitree	A Multi-task Approach to Open Domain Suggestion Mining Using Language Model for Text Over-Sampling	2020-03-24		.txt	text/plain	1569	105	59	title: A Multi-task Approach to Open Domain Suggestion Mining Using Language Model for Text Over-Sampling 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. In our study, we generate synthetic positive reviews till the number of suggestion and non-suggestion class samples becomes equal in the training set. All comparisons have been made in terms of the F-1 score of the suggestion class for a fair comparison with prior work on representational learning for open domain suggestion mining [5] (refer Baseline in Table 3 ). In this work, we proposed a Multi-task learning framework for Open Domain Suggestion Mining along with a novel language model based over-sampling technique for text-LMOTE.	./cache/cord-020794-d3oru1w5.txt	./txt/cord-020794-d3oru1w5.txt