Papers with neural topic modeling
Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts (2021.findings-emnlp)
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| Challenge: | Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Approach: | They develop a neural topic model which extracts topics from word co-occurrence graphs . Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. |
| Outcome: | Empirical results show that the proposed model can generate more coherent topics than baseline topic models. |
Towards the TopMost: A Topic Modeling System Toolkit (2024.acl-demos)
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| Challenge: | Current topic models adopt totally different datasets, implementations, and evaluations, hindering their research progress and applications. |
| Approach: | They propose a Topic Modeling System Toolkit that covers a broader spectrum of topic modeling scenarios with their complete lifecycles. |
| Outcome: | The proposed toolkit covers a broader spectrum of topic modeling scenarios with their complete lifecycles, including datasets, preprocessing, models, training, and evaluations. |
Neural Topic Modeling with Bidirectional Adversarial Training (2020.acl-main)
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| Challenge: | Recent studies have shown that neural topic models for automatic topic extraction avoid complicated mathematical derivations for model inference. |
| Approach: | They propose a bidirectional adversarial topic model which uses a generator and an encoder to infer topic distribution. |
| Outcome: | The proposed model outperforms baselines and competitive models in three benchmark corpora. |
Diversity-Aware Coherence Loss for Improving Neural Topic Models (2023.acl-short)
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| Challenge: | Experimental results show that our method significantly improves the performance of neural topic models without requiring any pretraining or additional parameters. |
| Approach: | They propose a variational autoencoder framework that minimizes the posterior and prior divergence and a diversity-aware coherence loss that encourages the model to learn corpus-level coherency scores while maintaining high diversity between topics. |
| Outcome: | The proposed approach significantly improves the performance of neural topic models without pretraining or additional parameters. |
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces (2021.naacl-main)
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| Challenge: | Recent work has used pre-trained word embeddings to address data sparsity in short-text or small document collections. |
| Approach: | They propose a neural topic modeling framework using multi-view embedding spaces to improve topic quality and deal with polysemy. |
| Outcome: | The proposed framework improves topic quality and deal with polysemy. |
Topic Modeling for Short Texts via Optimal Transport-Based Clustering (2025.findings-acl)
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| Challenge: | Existing approaches to topic modeling are based on probabilistic graphical models or non-negative matrix factorization techniques. |
| Approach: | They propose a method that aligns global clusters with topics to discover topics and learn document representations in topic space. |
| Outcome: | The proposed method outperforms state-of-the-art techniques in short-text topic modeling across commonly used metrics. |
Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge (2022.acl-long)
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| Challenge: | Recent studies have shown that using external knowledge such as pre-trained word embeddings or pre-train language models only achieved limited performance improvements but with huge computational overhead. |
| Approach: | They propose to incorporate external knowledge into neural topic modeling by pre-trained word embeddings (PWEs) or pre-train language models (PLMs) they propose to fine-tune the neural topic model on the target dataset and reduce the huge size of training data. |
| Outcome: | The proposed approach outperforms current state-of-the-art neural topic models and some topic modeling approaches enhanced with PWEs or PLMs on three datasets and greatly reduces the huge size of training data. |
Neural Topic Modeling with Cycle-Consistent Adversarial Training (2020.emnlp-main)
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| Challenge: | Recent advances on deep generative models have attracted significant interest in neural topic modeling. |
| Approach: | They propose an adversarial-neural topic model which uses Dirichlet prior to capture the semantic patterns in latent topics. |
| Outcome: | The proposed models outperform competing models on unsupervised/supervised topic modeling and text classification. |