| Challenge: | a novel topic model is proposed to allow topical trends to be captured in temporal collections of documents. |
| Approach: | They propose a novel unsupervised neural dynamic topic model where topics are influenced by topic discovery over time. |
| Outcome: | The proposed model shows better generalization, topic interpretation, evolution and trends compared to state-of-the-art models . |
Similar Papers
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections (P18-2)
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| Challenge: | a collection of documents often has dynamic structures, i.e., topics evolve along time depending on multiple topics in the past. |
| Approach: | They propose a dynamic and static topic model that considers dynamic and dynamic structures of topic evolution and static structures of the topic hierarchy at each time. |
| Outcome: | The proposed model outperforms conventional models on scientific papers . it shows that extracted topic structures are useful for analyzing research activities . |
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion (2024.findings-acl)
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| Challenge: | Existing dynamic topic models lack the ability to reveal the evolution of topics . Existing models suffer from repetitive topic and unassociated topic issues . |
| Approach: | They propose a new evolution-tracking contrastive learning method that builds the similarity relations among dynamic topics and an unassociated word exclusion method to avoid unassociated topics. |
| Outcome: | The proposed model outperforms state-of-the-art models on downstream tasks and is robust to evolution intensities. |
Beyond Coherence: Improving Temporal Consistency and Interpretability in Dynamic Topic Models (2026.findings-eacl)
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| Challenge: | Existing topic models capture bag-of-words statistics but lack semantic priors . interpretability remains shallow, relying on noisy top-word lists that obscure thematic clarity. |
| Approach: | They propose a variational framework to capture more faithful temporal trajectories . they propose to use entropy-regularized optimal transport to align entire topic constellations . |
| Outcome: | The proposed framework captures more faithful temporal trajectories and improves interpretability. |
Dynamic Structured Neural Topic Model with Self-Attention Mechanism (2023.findings-acl)
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| Challenge: | Recent topic models that capture the time-series evolution of topics assume that topics evolve independently without interaction. |
| Approach: | They propose a dynamic structured neural topic model which captures topic dependencies while capturing their dependencies. |
| Outcome: | The proposed model outperforms a prior dynamic embedded topic model regarding perplexity and coherence while maintaining sufficient diversity across topics. |
Tree-Structured Neural Topic Model (2020.acl-main)
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| Challenge: | Existing topic models do not organize topics into coherent groups or hierarchies. |
| Approach: | They propose a tree-structured neural topic model with an infinite number of branches and a topic distribution over a forest. |
| Outcome: | The proposed model improves data scalability and competitive performance when inducing latent topics and tree structures. |
Continual Neural Topic Model (2026.eacl-long)
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| Challenge: | Continual Neural Topic Models (CoNTMs) learn topic models at subsequent time steps without forgetting what was previously learned. |
| Approach: | They propose a Continual Neural Topic Model which continuously learns topic models at subsequent time steps without forgetting what was previously learned. |
| Outcome: | The proposed model outperforms the dynamic topic model in topic quality and predictive perplexity while being able to capture topic changes online. |
Dynamic Topic Modeling by Clustering Embeddings from Pretrained Language Models: A Research Proposal (2022.aacl-srw)
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| Challenge: | Neural Topic Models (NTMs) are topic models that are created with the help of a pretrained language model. |
| Approach: | They propose to do Neural Topic Modeling by Clustering document Embeddings (NTM-CE) with a pretrained language model to create dynamic topic models. |
| Outcome: | The proposed model can be evaluated theoretically and practically using quantitative measurements of coherence and human evaluation to evaluate the model. |
TAN-NTM: Topic Attention Networks for Neural Topic Modeling (2021.acl-long)
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| Challenge: | Topic models have been widely used to learn text representations and gain insight into document corpora. |
| Approach: | They propose a framework which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner. |
| Outcome: | The proposed model improves on two downstream tasks: document classification and topic guided keyphrase generation. |
Neural Topic Modeling with Large Language Models in the Loop (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, but their direct application to topic modeling suffers from issues such as incomplete topic coverage, misalignment of topics, and inefficiency. |
| Approach: | They propose a novel LLM-in-the-loop framework that integrates Large Language Models with Neural Topic Models (NTMs) global topics and document representations are learned through the NTM, while an LLM refines these topics using an Optimal Transport (OT)-based alignment objective. |
| Outcome: | The proposed framework improves topic interpretability while preserving the efficiency of existing NTMs. |
Context-Aware Neural Model for Temporal Information Extraction (P18-1)
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| Challenge: | Existing temporal information extraction systems rely on statistical learning with feature-engineered task-specific models. |
| Approach: | They propose a context-aware neural network model for temporal information extraction using a global context layer. |
| Outcome: | The proposed model outperforms existing models in terms of performance and performance . it is the first model to use NTM-like architecture to process the information from global context in discourse-scale natural text processing. |