Lifelong Machine Learning for Topic Modeling and Beyond
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Neural Topic Modeling with Continual Lifelong Learning
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 d…
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Topic modeling seeks to uncover latent semantic structure in text corpora with minimal supervision. Neural approaches achieve strong performance but require extensive tuning and struggle with lifelong learning due to cat…
Lifelong Neural Topic Learning in Contextualized Autoregressive Topic Models of Language via Informative Transfers
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 ord…
Data AugmentationHallucinationLanguage ModelingLanguage Modelling+2Findings of the First Shared Task on Lifelong Learning Machine Translation
A lifelong learning system can adapt to new data without forgetting previously acquired knowledge. In this paper, we introduce the first benchmark for lifelong learning machine translation. For this purpose, we provide t…
Lifelong learningMachine TranslationTranslationFast Online EM for Big Topic Modeling
The expectation-maximization (EM) algorithm can compute the maximum-likelihood (ML) or maximum a posterior (MAP) point estimate of the mixture models or latent variable models such as latent Dirichlet allocation (LDA), w…
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