Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings

Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents. However, no prior work has employed (pretrained latent) topics in transfer learning paradigm. In this paper, we propose a framework to perform transfer learning in neural topic modeling using (1) pretrained (latent) topics obtained from a large source corpus, and (2) pretrained word and topic embeddings jointly (i.e., multiview) in order to improve topic quality, better deal with polysemy and data sparsity issues in a target corpus. In doing so, we first accumulate topics and word representations from one or many source corpora to build respective pools of pretrained topic (i.e., TopicPool) and word embeddings (i.e., WordPool). Then, we identify one or multiple relevant source domain(s) and take advantage of corresponding topics and word features via the respective pools to guide meaningful learning in the sparse target domain. We quantify the quality of topic and document representations via generalization (perplexity), interpretability (topic coherence) and information retrieval (IR) using short-text, long-text, small and large document collections from news and medical domains. We have demonstrated the state-ofthe- art results on topic modeling with the proposed transfer learning approaches.