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I am solving many sequence-to-sequence prediction problems using RNN/LSTM.

What type of evaluation metrics can be used for sequence prediction problems?

One metric is the mean squared error (MSE) that we can give as a parameter during the training model. Currently, the accuracy of my sequence-to-sequence problems is very low.

What are other ways through which we can compare the performance of our models?

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    $\begingroup$ It depends on what type of data you're having and what kind of results you're interested in. For example you could use root mean square error (RMSE) which will greatly penalize large errors. If you want your results to be easily interpreted you could use mean absolute percentage error. $\endgroup$ – razvanc92 Nov 14 '19 at 7:37
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I would recommend taking a look at Bilingual Evaluation Understudy(BELU) score which is commonly used in evaluating machine translation results by sequence to sequence model. Here is the reference https://en.wikipedia.org/wiki/BLEU

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