论文标题
文本分类的模型混合
Model Blending for Text Classification
论文作者
论文摘要
深度神经网络（DNNS）已被证明在各种应用程序中都取得了成功，例如语音识别和合成，计算机视觉，机器翻译和游戏玩法，仅举几例。但是，现有的深度神经网络模型在计算上是昂贵且内存密集型的，阻碍了其在存储器资源低或具有严格延迟要求的应用程序中的部署。因此，一种自然的想法是在深网中执行模型压缩和加速度，而不会显着降低模型性能，这就是我们所说的降低复杂性。在以下工作中，我们尝试通过将其知识提炼为基于CNN的模型，从而降低自然语言任务（例如文本分类）的最佳状态LSTM模型的复杂性，从而减少了测试过程中的推理时间（或潜伏期）。
Deep neural networks (DNNs) have proven successful in a wide variety of applications such as speech recognition and synthesis, computer vision, machine translation, and game playing, to name but a few. However, existing deep neural network models are computationally expensive and memory intensive, hindering their deployment in devices with low memory resources or in applications with strict latency requirements. Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance, which is what we call reducing the complexity. In the following work, we try reducing the complexity of state of the art LSTM models for natural language tasks such as text classification, by distilling their knowledge to CNN based models, thus reducing the inference time(or latency) during testing.
