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The question is pretty straightforward, How well one can justify using LSTMs(Neural Networks) for text classification task in terms of "Generalization" compared to classic support vector machines(SVM) given that for text classification SVM works better most of the time in terms of evaluation metrics.

There are numerous advantages of using LSTMs when compared to using SVMs such as scalability, parameter sensitivity, etc which are well described everywhere but when it comes to generalization, there is not much to read. Hence the question remains "How can one justify using LSTMs for text classification in terms of generalization when compared to SVM?"

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