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Some papers say that BLEU is not a appropriate evaluating method for chatbot, instead they use perplexity to estimate chatbot.

First of all, what is perplexity? How to calculate it? And why is perplexity a good evaluation metric for chatbots?

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With perplexity you are trying to evaluate the similarity between the token (in your case probably sentences) distribution generated by the model and the one in the test data.

For instance, assuming you have $M$ sentences $s_1, \dots, s_M$, each with probability $P(s_i)$, the perplexity is $$2^{-l},$$ where $l = \frac{1}{M} \sum P(s_i) \log P(s_i)$ for $i \in [1 \dots M]$.

Note that while perplexity might be useful to capturing certain aspects of the model, it is by no means perfect, and, even if you are able to reach great perplexity scores, it will not necessarily translate to a good or even working chat bot.

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