title: Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings
abstract: 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.
authors: Pankaj Gupta Yatin Chaudhary Hinrich Schütze
hIndex: Hinrich Schütze 56