In this paper, we present a new topic modelling approach via the theory of optimal transport (OT). Specifically, we present a document with two distributions: a distribution over the words (doc-word distribution ) and a distribution over the topics (doc-topic distribution). For one document, the doc-word distribution is the observed, sparse, low-level representation of the content, while the doc-topic distribution is the latent, dense, high-level one of the same content. Learning a topic model can then be viewed as a process of minimising the transportation of the semantic information from one distribution to the other. This new viewpoint leads to a novel OT-based topic modelling framework, which enjoys appealing simplicity, effectiveness, and efficiency. Extensive experiments show that our framework significantly outperforms several state-of-the-art models in terms of both topic quality and document representations .
Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it is usually hard for existing NTMs to achieve good document representation and coherent/diverse topics at the same time. Moreover, they often degrade their performance severely on short documents. The requirement of reparameterisation could also comprise their training quality and model flexibility. To address these shortcomings, we present a new neural topic model via the theory of optimal transport (OT). Specifically, we propose to learn the topic distribution of a document by directly minimising its OT distance to the document's word distributions. Importantly, the cost matrix of the OT distance models the weights between topics and words, which is constructed by the distances between topics and words in an embedding space. Our proposed model can be trained efficiently with a differentiable loss. Extensive experiments show that our framework significantly outperforms the state-of-the-art NTMs on discovering more coherent and diverse topics and deriving better document representations for both regular and short texts .
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In this paper,
Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it is usually hard for existing NTMs to achieve good document representation and coherent/diverse topics at the same time. Moreover, they often degrade their performance severely on short documents. The requirement of reparameterisation could also comprise their training quality and model flexibility. To address these shortcomings,
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topic modelling approach
neural topic model
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present a document with two distributions: a distribution over the words (doc-word distribution ) and a distribution over the topics (doc-topic distribution). For one document, the doc-word distribution is the observed, sparse, low-level representation of the content, while the doc-topic distribution is the latent, dense, high-level one of the same content. Learning a topic model can then be viewed as a process of minimising the transportation of the semantic information from one distribution to the other. This new viewpoint leads to a novel OT-based topic modelling framework, which enjoys appealing simplicity, effectiveness, and efficiency.
propose to learn the topic distribution of a document by directly minimising its OT distance to the document's word distributions. Importantly, the cost matrix of the OT distance models the weights between topics and words, which is constructed by the distances between topics and words in an embedding space. Our proposed model can be trained efficiently with a differentiable loss.
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several
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models in terms of both topic quality and document representations
NTMs on discovering more coherent and diverse topics and deriving better document representations for both regular and short texts