| Challenge: | Neural topic modeling has been attracting much attention recently due to its ability to leverage the advantages of both neural networks and probabilistic topic models. |
| Approach: | They propose to evaluate neural topic models in three tasks using large datasets and a set of metrics to compare them. |
| Outcome: | The proposed models perform better in the first and third tasks than the traditional probabilistic models and are better in many cases. |
Similar Papers
Are Neural Topic Models Broken? (2022.findings-emnlp)
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| Challenge: | Existing evaluation paradigms are often divorced from real-world use . recent results have challenged the validity of the prevailing model evaluation paradigm . |
| Approach: | They show that neural topic models fare worse in both respects compared to an established classical method. |
| Outcome: | The proposed method outperforms the members of the ensemble in both respects. |
Neural Multimodal Topic Modeling: A Comprehensive Evaluation (2024.lrec-main)
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| Challenge: | Neural topic models can find coherent and diverse topics in textual data, but they are limited in dealing with multimodal datasets. |
| Approach: | They propose two new topic modeling solutions and two new evaluation metrics for document multimodality. |
| Outcome: | The proposed models generate coherent and diverse topics on a rich dataset. |
Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis (2024.eacl-long)
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Zongxia Li, Andrew Mao, Daniel Stephens, Pranav Goel, Emily Walpole, Alden Dima, Juan Fung, Jordan Boyd-Graber
| Challenge: | Existing evaluation metrics such as coherence and coherency are inadequate for neural topic models. |
| Approach: | They conduct the first evaluation of neural, supervised and classical topic models in an interactive task-based setting. |
| Outcome: | The proposed model performs better on cluster evaluation metrics and human evaluations than classical models on real-world tasks. |
Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics (2022.naacl-main)
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| Challenge: | Recent work incorporates pre-trained word embeddings into Neural Topic Models (NTMs), generating highly coherent topics. |
| Approach: | They conduct thorough experiments to investigate whether embeddings directly with an appropriate word selection method can generate more coherent and diverse topics than NTMs. |
| Outcome: | The proposed model generates more coherent and diverse topics than traditional NTMs, achieving higher efficiency and simplicity. |
Topic Modeling: Contextual Token Embeddings Are All You Need (2024.findings-emnlp)
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| Challenge: | Current neural approaches to topic modeling have not been able to solve all of the problems. |
| Approach: | They propose a topic modeling approach that uses document contextual token embeddings to find topics and find topic spans within documents. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on a comprehensive set of topic model evaluation metrics. |
Coherence-Aware Neural Topic Modeling (D18-1)
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| Challenge: | Topic models are evaluated for their ability to describe documents well (i.e. low perplexity) topic coherence is not optimized for and is only evaluated after training. |
| Approach: | They propose to incorporate a topic coherence objective into the training process by incorporating a coherency objective into a model. |
| Outcome: | The proposed model exhibits similar level of perplexity as baseline models but significantly higher topic coherence. |
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence (2021.acl-short)
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| Challenge: | Recent neural topic models extract words from documents, but they are not coherent . coherence is crucial for topic models, but many use bag-of-words document representations as input . pre-trained language models are becoming ubiquitous in natural language processing . |
| Approach: | They combine contextualized representations with neural topic models to produce more coherent topics . they say that future improvements in language models will translate into better topic models . |
| Outcome: | The proposed approach produces more meaningful and coherent topics than bag-of-words models and recent neural models. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks (2024.lrec-main)
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| Challenge: | Topic models aim to reveal latent structures within corpus of text through term-frequency statistics over bag-of-words representations. |
| Approach: | They propose to use bimodal vector representations of entities to extract latent representations from large language models and graph neural networks trained on symbolic relations to derive the most salient aspects of these conceptual units. |
| Outcome: | The proposed approach is better suited to working with entities than state-of-the-art models. |
Exploring Neural Topic Modeling on a Classical Latin Corpus (2024.lrec-main)
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| Challenge: | Using topic modeling, it is possible to study Latin literature through methods and tools that support distant reading. |
| Approach: | They propose to use topic modeling to investigate thematic distribution of Latin corpus . they train, optimize and compare two neural models to evaluate which performs better . |
| Outcome: | The proposed model is compared with two neural models with a Classical Latin corpus and shows that it is coherent and interpretable. |