Yoon Kim’s research while affiliated with The Graduate Center, CUNY and other places

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We first show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static word vectors. The CNN models discussed herein improve upon the state-of-the-art on 4 out of 7 tasks, which include sentiment analysis and question classification.