This paper presents a series of experiments using convolutional neural networks (CNNs) trained on top of pre-trained word vectors for sentence-level classification tasks. The authors demonstrate that a simple CNN with minimal hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning further improves performance. They also propose a modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed in the paper improve upon the state of the art on 4 out of 7 tasks, including sentiment analysis and question classification. The work is philosophically similar to previous research showing that pre-trained deep learning models can perform well on a variety of tasks, even those very different from the original task. The paper includes a detailed description of the model architecture, experimental setup, and results, highlighting the effectiveness of pre-trained word vectors and the benefits of fine-tuning.This paper presents a series of experiments using convolutional neural networks (CNNs) trained on top of pre-trained word vectors for sentence-level classification tasks. The authors demonstrate that a simple CNN with minimal hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning further improves performance. They also propose a modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed in the paper improve upon the state of the art on 4 out of 7 tasks, including sentiment analysis and question classification. The work is philosophically similar to previous research showing that pre-trained deep learning models can perform well on a variety of tasks, even those very different from the original task. The paper includes a detailed description of the model architecture, experimental setup, and results, highlighting the effectiveness of pre-trained word vectors and the benefits of fine-tuning.