问题描述
当我阅读卷积神经网络用于句子分类"(Yoon Kim-纽约大学)时，我注意到该论文实现了"CNN-非静态"模型-该模型具有来自word2vec的预训练向量，所有单词(包括随机初始化的未知单词)，以及针对每个任务的预训练矢量都将进行微调.因此，我只是不了解如何针对每个任务微调预训练向量.据我所知，输入向量是通过word2vec.bin(预训练)从字符串转换而来的，就像图像矩阵一样，在训练CNN时不会改变.那么，如果可以，怎么办?请帮帮我，非常感谢！
When I read the paper "Convolutional Neural Networks for Sentence Classification"-Yoon Kim-New York University, I noticed that the paper implemented the "CNN-non-static" model--A model with pre-trained vectors from word2vec,and all words— including the unknown ones that are randomly initialized, and the pre-trained vectors are fine-tuned for each task.So I just do not understand how the pre-trained vectors are fine-tuned for each task. Cause as far as I know, the input vectors, which are converted from strings by word2vec.bin(pre-trained), just like image matrix, which can not change during training CNN. So, if they can, HOW? Please help me out, Thanks a lot in advance!
推荐答案
词嵌入是神经网络的权重，因此可以在反向传播期间进行更新.
The word embeddings are weights of the neural network, and can therefore be updated during backpropagation.
例如 http://sebastianruder.com/word-embeddings-1/:
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