Method of and system for multi-view and multi-source transfers in neural topic modelling

A computer-implemented method of Neural Topic Modelling, NTM, in an autoregressive Neural Network, NN, using Global-View Transfer, GVT, for a probabilistic or neural autoregressive topic model of a target T given a document νof words νi, i=1, . . . D, comprising the steps:

• preparing a pre-trained topic Knowledge Base, KB, of latent topic features Zk ∈H×K, where k indicates the number of a source Sk , k≥1, of the latent topic feature, H indicates the dimension of the latent topic and K indicates a vocabulary size;
• transferring knowledge to the target T by GVT via learning meaningful latent topic features guided by relevant latent topic features Zk of the topic KB, comprising the sub-step:
• extending a loss function (ν) of the probabilistic or neural autoregressive topic model for the document ν of the target T, which loss function (ν) is a negative log-likelihood of j oint probabilities p(νi|νν<i) of each word νi in the autoregressive NN which probabilities p(νi|ν<i) for each word νi are based on the preceding words ν<i, with a regularisation term comprising weighted relevant latent topic features Zk to form a extended loss function reg(ν);
• and
• minimising the extended loss function reg (ν) to determine a minimal overall loss.