  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 in short-text or small collection of documents. This
work presents a novel neural topic modeling framework using multi-view
embedding spaces: (1) pretrained topic-embeddings, and (2) pretrained
word-embeddings (context insensitive from Glove and context-sensitive from BERT
models) jointly from one or many sources to improve topic quality and better
deal with polysemy. In doing so, we first build respective pools of pretrained
topic (i.e., TopicPool) and word embeddings (i.e., WordPool). We then identify
one or more relevant source domain(s) and transfer knowledge to guide
meaningful learning in the sparse target domain. Within neural topic modeling,
we quantify the quality of topics 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. Introducing the multi-source
multi-view embedding spaces, we have shown state-of-the-art neural topic
modeling using 6 source (high-resource) and 5 target (low-resource) corpora.
