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=Introduction= | =Introduction= | ||
Topic Compositional Neural Language Model (TCNLM) simultaneously captures both the global semantic meaning and the local word-ordering structure in a document. A common TCNLM incorporates fundamental components of both a neural topic model (NTM) and a Mixture-of-Experts (MoE) language model. The latent topics learned within a variational autoencoder framework, coupled with the probability of topic usage, are further trained in a MoE model. (Insert figure here) | |||
TCNLM networks are well-suited for topic classification and sentence generation on a given topic. The combination of latent topics, weighted by the topic-usage probabilities, yields an effective prediction for the sentences. TCNLMs were also developed to address the incapability of RNN-based neural language models in capturing broad document context. After learning the global semantic, the probability of each learned latent topic is used to learn the local structure of a word sequence. | |||
=Topic Model= | =Topic Model= | ||
Revision as of 11:55, 5 November 2018
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Introduction
Topic Compositional Neural Language Model (TCNLM) simultaneously captures both the global semantic meaning and the local word-ordering structure in a document. A common TCNLM incorporates fundamental components of both a neural topic model (NTM) and a Mixture-of-Experts (MoE) language model. The latent topics learned within a variational autoencoder framework, coupled with the probability of topic usage, are further trained in a MoE model. (Insert figure here)
TCNLM networks are well-suited for topic classification and sentence generation on a given topic. The combination of latent topics, weighted by the topic-usage probabilities, yields an effective prediction for the sentences. TCNLMs were also developed to address the incapability of RNN-based neural language models in capturing broad document context. After learning the global semantic, the probability of each learned latent topic is used to learn the local structure of a word sequence.