Number of parameters in an RNN I'm using a basic RNN as in the figure below (say for translation).
The model has the following structure:

\begin{aligned} s_t &amp;= \tanh(Ux_t + Ws_{t-1}) \\ o_t &amp;= \mathrm{softmax}(Vs_t) \end{aligned} 


Assume m is the vocabulary size and n that of the hidden layer.
If $x_{t}=\{0,1\}^{m}$ and U is a $ n \times m$  matrix then W is a $ n \times n $ matrix. 
If $o_{t}$ is $\mathbb{R}^{k}$ and $s_{t}$ is $\mathbb{R}^{n}$then V is a $ k \times n $ matrix. 


What's the # parameters for this RNN model?
The entities W , U and V are shared by all steps of the RNN and these are the only parameters in the model described in the figure. Hence number of parameters to be learnt while training = $dim(W)+ dim(V)+ dim(U)$. 

Based on data in the question this = $ n^{2}+ kn + nm$. 

where,


n - dimension of hidden layer
k - dimension of output layer
m - dimension of input layer