Adaptive Convolution for Text Classification

In this paper, we present an adaptive convolution for text classification to give stronger flexibility to convolutional neural networks (CNNs). Unlike traditional convolutions that use the same set of filters regardless of different inputs, the adaptive convolution employs adaptively generated convolutional filters that are conditioned on inputs. We achieve this by attaching filter-generating networks, which are carefully designed to generate input-specific filters, to convolution blocks in existing CNNs. We show the efficacy of our approach in existing CNNs based on our performance evaluation. Our evaluation indicates that adaptive convolutions improve all the baselines, without any exception, as much as up to 2.6 percentage point in seven benchmark text classification datasets.