Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers

29 Sep 2019Yatin ChaudharyPankaj GuptaThomas Runkler

Topic models such as LDA, DocNADE, iDocNADEe have been popular in document analysis. However, the traditional topic models have several limitations including: (1) Bag-of-words (BoW) assumption, where they ignore word ordering, (2) Data sparsity, where the application of topic models is challenging due to limited word co-occurrences, leading to incoherent topics and (3) No Continuous Learning framework for topic learning in lifelong fashion, exploiting historical knowledge (or latent topics) and minimizing catastrophic forgetting... (read more)

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