Convolutional Neural Networks Based Multi-task Deep Learning for Movie Review Classification

Abstract

Deep learning has achieved impressive success in natural language processing. However, most previous models are learned on the specific single tasks, suffering from insufficient training set. Multi-task deep learning can solve this dilemma by sharing the part of parameters, improving generalization. The common multi-task deep learning model consists of the shared layer and the task specific layer. In this paper, we attempt to enhance the performance of shared layer and proposed two variants based on convolutional neural networks. The first model is Agent Model-Direct Concatenate, where each task is assigned with a separate convolutional neural network for extracting the common and task specific features simultaneously. The second model is Agent Model-Gating Concatenation, where the task specific layer could automatically decide the information flow of each element of the output of shared layer. The two networks are trained jointly over three pair-wise groups of movie review data sets. Experiments show the effectiveness of our two networks, inspiring a potential direction for the related research of multi-task deep learning.