Paper: Convolutional Neural Networks for Sentence Classification

ACL ID D14-1181
Title Convolutional Neural Networks for Sentence Classification
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vec- tors for sentence-level classification tasks. We show that a simple CNN with lit- tle hyperparameter tuning and static vec- tors achieves excellent results on multi- ple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the ar- chitecture to allow for the use of both task-specific and static 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.