Topic Memory Networks for Short Text Classification
Jichuan Zeng, Jing Li, Yan Song, Cuiyun Gao, Michael Rung-Tsong Lyu, Irwin King · 2018
Many classification models work poorly on short texts due to data sparsity.To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels.Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner.Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.* This work was mainly conducted when Jichuan Zeng was an intern in Tencent AI Lab.† Jing Li is the corresponding author.Training instances R1: [SuperBowl] I'll do anything to see the Steelers win.R2: [New.Music.Live] Please give wristbands, she have major Bieber Fever.