2
$\begingroup$

In computer vision, if we don't have a large training set, a common method is to start with a pre-trained model for some related task (e.g., ImageNet) and fine-tune that model to solve our problem.

Can something similar be done with natural language processing problems? I have a boolean classification problem on sentences and don't have a large enough training set to train a RNN from scratch. In particular, is there a good way to fine-tune a LSTM or 1D CNN or otherwise do transfer learning? And, if we want to do classification on sentences, is there a reasonable pre-trained model to start from?

$\endgroup$

1 Answer 1

2
$\begingroup$

This paper might be useful....

https://arxiv.org/abs/1801.06146

$\endgroup$
1

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.