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Diversity-Aware Coherence Loss for Improving Neural Topic Models

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Raymond Li, Felipe González-Pizarro, Linzi Xing, Gabriel Murray, Giuseppe Carenini

Computation and Language · Submitted 25 May 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results on multiple datasets show that our method significantly improves the performance of neural topic models without requiring any pretraining or additional parameters.

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arXiv ID2305.16199 (version 2)
Pith ID5RZZNGIJ, Pith's permanent record of this paper and its review
CitationFelipe Gonz\'alez-Pizarro, Gabriel Murray, Giuseppe Carenini, Linzi Xing, Raymond Li. "Diversity-Aware Coherence Loss for Improving Neural Topic Models." arXiv:2305.16199 (2023). https://pith.science/paper/5RZZNGIJ
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,616 input tokens.

What the abstract itself states, as probabilities: claims first 0.08 · states limitation 0.07 · human participants 0.03 · controlled comparison 0.41 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.59 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; .

The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss. Since neural topic models are trained by recreating individual input documents, they do not explicitly capture the coherence between topic words on the corpus level. In this work, we propose a novel diversity-aware coherence loss that encourages the model to learn corpus-level coherence scores while maintaining a high diversity between topics. Experimental results on multiple datasets show that our method significantly improves the performance of neural topic models without requiring any pretraining or additional parameters.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss. background 1.00 0.04 0.02 asserted 1.00
s1 Since neural topic models are trained by recreating individual input documents, they do not explicitly capture the coherence between topic words on the corpus level. gap 0.84 0.09 0.03 asserted 1.00
s2 In this work, we propose a novel diversity-aware coherence loss that encourages the model to learn corpus-level coherence scores while maintaining a high diversity between topics. contribution 0.86 0.80 0.18 asserted 0.97
s3 Experimental results on multiple datasets show that our method significantly improves the performance of neural topic models without requiring any pretraining or additional parameters. result 1.00 0.96 0.84 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.95.

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  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss. Since neural topic models are trained by recreating individual input documents, they do not explicitly capture the coherence between topic words on the corpus level. In this work, we propose a novel diversity-aware coherence loss that encourages the model to learn corpus-level coherence scores while maintaining a high diversity between topics. Experimental results on multiple datasets show that our method significantly improves the performance of neural topic models without requiring any pretraining or additional parameters.

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