Topically Driven Neural Language Model

ACL 2017

Topically Driven Neural Language Model

Jan 22, 2021
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Abstract: Language models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context in the form of a topic model-like architecture, thus providing a succinct representation of the broader document context outside of the current sentence. Experiments over a range of datasets demonstrate that our model outperforms a pure sentence-based model in terms of language model perplexity, and leads to topics that are potentially more coherent than those produced by a standard LDA topic model. Our model also has the ability to generate related sentences for a topic, providing another way to interpret topics. Authors: Jey Han Lau, Timothy Baldwin, Trevor Cohn (IBM Research, The University of Melbourne)

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