However previous work (Zhang et al., 2017) has noted that word frequency information is a strong signal -words with similar frequency are likely to be matched -and thus we use π(y j |x i ) to incorporate this prior knowledge.
in this work, we assume that the frequencies of aligned bilingual words in the individual monolingual corpus should be correlated, and to match words that are ranked similarly, we model the Gaussian mixture weights as the negative absolute difference between log-scale word ranks and normalize over all the target Gaussian component words by a softmax function with temperature τ : if a word x i has similarly frequency rank as word y j , the sample x from the Gaussian distribution centered at x i will be assigned higher weight for the componentp(y|y j ) =p(W xy x|y j ).