Convolutional Neural Networks for Sentence Classification

EMNLP 2014  Â·  Yoon Kim ·

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 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 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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Emotion Recognition in Conversation CPED TextCNN Accuracy of Sentiment 48.90 # 6
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Macro-F1 of Sentiment 34.37 # 10
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Natural Language Inference SNLI CNN-MC [[Kim2014]] % Test Accuracy 88.1 # 36
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Sentiment Analysis SST-2 Binary classification CNN-MC [kim:13] Accuracy 88.1 # 67
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