METHOD OF AND SYSTEM FOR MULTI-VIEW AND MULTI-SOURCE TRANSFERS IN NEURAL TOPIC MODELLING
The present invention relates to a computer-implemented method of Neural Topic Modelling (NTM), a respective computer program, computer-readable medium and data processing system. Global-View Transfer (GVT) or Multi-View Transfer (MTV, GVT and Local-View Transfer (LVT) jointly applied), with or without Multi-Source Transfer (MST) are utilised in the method of NTM. For GVT a pre-trained topic Knowledge Base (KB) of latent topic features is prepared and knowledge is transferred to a target by GVT via learning meaningful latent topic features guided by relevant latent topic features of the topic KB. This is effected by extending a loss function and minimising the extended loss function. For MVT additionally a pre-trained word embeddings KB of word embeddings is prepared and knowledge is transferred to the target by LVT via learning meaningful word embeddings guided by relevant word embeddings of the word embeddings KB. This is effected by extending a term for calculating pre-activations.
1 . 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 ν i , i=1, . . . D, comprising the steps:
preparing a pre-trained topic Knowledge Base, KB, of latent topic features Z k ∈ H×K , where k indicates the number of a source S k , 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 Z k 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(ν i |νν <i ) of each word ν i in the autoregressive NN which probabilities p(ν i |ν <i ) for each word ν i are based on the preceding words ν <i , with a regularisation term comprising weighted relevant latent topic features Z k to form a extended loss function reg (ν);
and
minimising the extended loss function reg (ν) to determine a minimal overall loss.
2 . The computer-implemented method according to claim 1 , wherein the probabilistic or neural autoregressive topic model is a DocNADE architecture.
3 . The computer-implemented method according to claim 1 , using Multi-View Transfer, MVT, by additionally using Local-View Transfer, LVT, further comprising the primary steps:
preparing a pre-trained word embeddings KB of word embeddings E k ∈ E×K , where E indicates the dimension of the word embedding;
transferring knowledge to the target T by LVT via learning meaningful word embeddings guided by relevant word embeddings E k of the word embeddings KB, comprising the sub-step:
extending a term for calculating pre-activations α of the probabilistic or neural autoregressive topic model of the target T, which pre-activations α control an activation of the autoregressive NN for the preceding words ν <i in the probabilities p(ν i |ν <i ) of each word ν i , with weighted relevant latent word embeddings E k to form an extended pre-activation α ext .
4 . The computer-implemented method according to claim 1 using Multi-Source Transfer, MST, wherein the latent topic features Z k ∈ H×K of the topic KB and/or the word embeddings E k ∈ E×K of the word embeddings KB stem from more than one source S k , k>1.
5 . The computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method according to claim 1 .
6 . The computer-readable medium having stored thereon the computer program according to claim 5 .
7 . A data processing system comprising means for carrying out the steps of the method according to claim 1 .