  Lifelong learning has recently attracted attention in building machine
learning systems that continually accumulate and transfer knowledge to help
future learning. Unsupervised topic modeling has been popularly used to
discover topics from document collections. However, the application of topic
modeling is challenging due to data sparsity, e.g., in a small collection of
(short) documents and thus, generate incoherent topics and sub-optimal document
representations. To address the problem, we propose a lifelong learning
framework for neural topic modeling that can continuously process streams of
document collections, accumulate topics and guide future topic modeling tasks
by knowledge transfer from several sources to better deal with the sparse data.
In the lifelong process, we particularly investigate jointly: (1) sharing
generative homologies (latent topics) over lifetime to transfer prior
knowledge, and (2) minimizing catastrophic forgetting to retain the past
learning via novel selective data augmentation, co-training and topic
regularization approaches. Given a stream of document collections, we apply the
proposed Lifelong Neural Topic Modeling (LNTM) framework in modeling three
sparse document collections as future tasks and demonstrate improved
performance quantified by perplexity, topic coherence and information retrieval
task.
