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Subtext is a kind of deep semantics which can be acquired after one or morerounds of expression transformation. As a popular way of expressing one'sintentions, it is well worth studying. In this paper, we try to make computersunderstand whether there is a subtext by means of machine learning. We build aChinese dataset whose source data comes from the popular social media (e.g.Weibo, Netease Music, Zhihu, and Bilibili). In addition, we also build abaseline model called SASICM to deal with subtext recognition. The F1 score ofSASICMg, whose pretrained model is GloVe, is as high as 64.37%, which is 3.97%higher than that of BERT based model, 12.7% higher than that of traditionalmethods on average, including support vector machine, logistic regressionclassifier, maximum entropy classifier, naive bayes classifier and decisiontree and 2.39% higher than that of the state-of-the-art, including MARIN andBTM. The F1 score of SASICMBERT, whose pretrained model is BERT, is 65.12%,which is 0.75% higher than that of SASICMg. The accuracy rates of SASICMg andSASICMBERT are 71.16% and 70.76%, respectively, which can compete with those ofother methods which are mentioned before.

SASICM A Multi-Task Benchmark For Subtext Recognition