TOPIC MODELING

CASE: Large Scale Topic Exploitation for Decision Support Systems (2025.coling-demos)

Challenge:	Large Language Models (LLMs) can be used to solve topic modeling challenges for short texts by contextually learning the meanings of words.
Approach:	They propose two approaches to using Large Language Models (LLMs) for topic modeling: parallel prompting and sequential prompting.
Outcome:	The proposed methods identify more coherent topics than existing ones while maintaining the diversity of the induced topics.

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Lorena Calvo Bartolomé, Jerónimo Arenas-García, David Pérez Fernández

Topic Modeling

Challenge:	Topic models are still a major tool for information retrieval and summarization, but their integration into decision-making systems is limited.
Approach:	They propose a tool for exploiting topic information for semantic analysis of large corpora using a Solr engine and a customized indexing strategy.
Outcome:	The proposed approach can be used to analyze large corpora and perform thematic trend analysis, topic-based document retrieval, or similarity search.

Discovering Topics in Long-tailed Corpora with Causal Intervention (2021.findings-acl)

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Xiaobao Wu, Chunping Li, Yishu Miao

STREAM-ZH: Simplified Topic Retrieval Exploration and Analysis Module for Chinese Language (2026.eacl-short)

Challenge:	Existing topic models are designed and evaluated on balanced corpora, but the longtailed bias can impair the performance of topic models.
Approach:	They propose a causal inference framework to explain topic modeling on long-tailed corpora by applying causal intervention in training.
Outcome:	The proposed model can mitigate the bias effect, greatly improve topic quality and discover the hidden semantics on the tail.

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Hongyi Li, Jianjun Lian, Anton Frederik Thielmann, Andre Python

Variable Typing: Assigning Meaning to Variables in Mathematical Text (N18-1)

Challenge:	Simplified Topic Retrieval Exploration and Analysis Module for Chinese language is the first topic modeling package to fully support the Chinese language.
Approach:	They propose a topic modeling package that fully supports the Chinese language . they use preprocessed textual datasets to assess topic models .
Outcome:	The proposed framework outperforms existing topic models using English-translated textual input.

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Yiannos Stathopoulos, Simon Baker, Marek Rei, Simone Teufel

Numerical Reasoning

Challenge:	Scientific documents rely on mathematics to communicate ideas and results . textual contexts are strong domainspecific conventions governing how content is presented .
Approach:	They introduce a task of assigning one mathematical type to each variable in a sentence . they also introduce 'variable typing' task that focuses on assignment of meaning to variables .
Outcome:	The proposed model is the best performing model on an extrinsic task, the authors show . their model is compared to a formula index only containing raw symbols .

Personalized Benchmarking: Evaluating LLMs by Individual Preferences (2026.findings-acl)

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Cristina Garbacea, Heran Wang, Chenhao Tan

Challenge:	Current benchmarks average preferences across all users to compute aggregate ratings . this overlooks individual user preferences when establishing model rankings .
Approach:	They compute personalized model rankings using ELO ratings and Bradley-Terry coefficients . they find users exhibit substantial heterogeneity in topical interests and communication styles .
Outcome:	The results show that individual rankings of LLM models diverge dramatically from aggregate rankings . a compact combination of topic and style features provides a useful feature space .

S3 - Semantic Signal Separation (2025.acl-long)

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Márton Kardos, Jan Kostkan, Kenneth Enevoldsen, Arnault-Quentin Vermillet, Kristoffer Nielbo, Roberta Rocca

Topic Modeling

Challenge:	Recent efforts to incorporate contextual representations into topic models have been shown to outperform classical topic models.
Approach:	They propose a theory-driven topic modeling approach that decomposes contextualized document embeddings into a Python package that implements S3 and all contextual baselines.
Outcome:	The proposed model is 4.5x faster than the BERTopic model and provides diverse and highly coherent topics with no preprocessing.

Investigating Political Herd Mentality: A Community Sentiment Based Approach (P19-2)

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Anjali Bhavan, Rohan Mishra, Pradyumna Prakhar Sinha, Ramit Sawhney, Rajiv Ratn Shah

STREAM: Simplified Topic Retrieval, Exploration, and Analysis Module (2024.acl-short)

Challenge:	polarities inherent in political speeches and debates pose an important problem today.
Approach:	They propose to use community-based graphs to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts.
Outcome:	The proposed approach surpasses the benchmark results on the same dataset.

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Anton Thielmann, Arik Reuter, Christoph Weisser, Gillian Kant, Manish Kumar, Benjamin Säfken

A Lightweight Approach to a Giga-Corpus of Historical Periodicals: The Story of a Slovenian Historical Newspaper Collection (2024.lrec-main)

Challenge:	Topic modeling is a widely used technique to analyze large document corpora.
Approach:	They propose a module for topic retrieval, exploration, and analysis that implements multiple intruder-word based topic evaluation metrics.
Outcome:	The proposed module implements multiple intruder-word based topic evaluation metrics and extends existing datasets.

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Filip Dobranić, Bojan Evkoski, Nikola Ljubešić

Challenge:	a curated corpus of Slovenian historical newspapers is a complex undertaking requiring multiple steps to prepare . a shoestring budget is required to produce a corpus that is billion-words in size .
Approach:	They propose a lightweight approach to producing high-quality corpora using OCR . they use noisy OCR-ed data from the National and University Library of Slovenia .
Outcome:	The proposed method produces a billion-word giga-corpus of Slovenian historical newspapers from the 18th, 19th and 20th centuries on a shoestring budget.

Topic Modeling by Clustering Language Model Embeddings: Human Validation on an Industry Dataset (2022.emnlp-industry)

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Anton Eklund, Mona Forsman

Neural Topic Modeling with Large Language Models in the Loop (2025.acl-long)

Challenge:	Topic models are powerful tools to get overview of large collections of text data.
Approach:	They propose to use a tool called STELLAR for interactive topic browsing to evaluate topics from a real-world dataset.
Outcome:	The proposed model performs better than LDA models in the real-world and is scalable.

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Xiaohao Yang, He Zhao, Weijie Xu, Yuanyuan Qi, Jueqing Lu, Dinh Phung, Lan Du

QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums (2025.findings-naacl)

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.

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Varun Nagaraj Rao, Eesha Agarwal, Samantha Dalal, Dana Calacci, Andrés Monroy-Hernández

Multimodal Topic-Enriched Auxiliary Learning for Depression Detection (2020.coling-main)

Challenge:	Qualitative and quantitative methods to analyze text data on online forums are infeasible to scale or require significant human effort to translate outputs to human readable forms.
Approach:	They propose a novel LLM-based framework to analyze and extract quantitative insights from text data on online forums.
Outcome:	The proposed framework analyzes over one million comments from two of Reddit’s rideshare worker communities, marking the largest study of its type.

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Minghui An, Jingjing Wang, Shoushan Li, Guodong Zhou

Multimodality

Challenge:	Existing studies on depression detection rely on textual and visual content to determine whether a human being is depressed or non-depressed.
Approach:	They propose a multimodal topic-enriched Auxiliary Learning approach that captures topic information from texts and images for depression detection.
Outcome:	The proposed approach improves the performance of the primary task by using topic information from text and images.

Coherence-Aware Neural Topic Modeling (D18-1)

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Ran Ding, Ramesh Nallapati, Bing Xiang

Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process (N19-1)

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.

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Dingcheng Li, Siamak Zamani, Jingyuan Zhang, Ping Li

A Novel Framework Based on Medical Concept Driven Attention for Explainable Medical Code Prediction via External Knowledge (2022.findings-acl)

Challenge:	Topic models are used to extract topical structures from document-word frequency representations of the text corpus without supervision.
Approach:	They propose a Bayesian nonparametric topic modeling with knowledge graph embedding to employ knowledge graphs to extract more coherent topics.
Outcome:	The proposed model performs better on three public datasets than state-of-the-art models on topic coherence and document classification accuracy.

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Tao Wang, Linhai Zhang, Chenchen Ye, Junxi Liu, Deyu Zhou

Explainability & Interpretability in NLP

Challenge:	Existing methods to predict medical codes from clinical notes lack interpretability due to lengthy and noisy clinical notes.
Approach:	They propose a framework based on medical concept driven attention to integrate external knowledge for explainable medical code prediction from clinical notes.
Outcome:	The proposed framework outperforms state-of-the-art methods on a benchmark dataset showing that it is more accurate than existing methods.

Hierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings (2025.acl-long)

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Hans William Alexander Hanley, Zakir Durumeric

FIDELITY: Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding (2025.findings-naacl)

Challenge:	Contextual large language model embeddings are often monolingual, do not scale, and struggle in multilingual settings.
Approach:	They propose a hierarchical approach to embed news articles and social media data using Matryoshka embeddings that can determine story similarity at varying levels of granularity based on which subset of dimensions is examined.
Outcome:	The proposed model achieves state-of-the-art performance on the SemEval 2022 task 8 dataset.

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Divyansh Singh, Brodie Mather, Demi Zhang, Patrick Lehman, Justin Ho, Bonnie J Dorr

Combining Deep Learning and Topic Modeling for Review Understanding in Context-Aware Recommendation (N18-1)

Challenge:	Existing methods for topic modeling generate contextually specific and semantically intuitive topics, especially in dynamic environments and low-resource languages.
Approach:	They propose a hybrid method that combines topic modeling and text summarization to produce fine-grained, semantically rich, and contextually relevant output.
Outcome:	FIDELITY outperforms traditional models in topic diversity, similarity, and ability to process new, unseen documents.

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Mingmin Jin, Xin Luo, Huiling Zhu, Hankz Hankui Zhuo

A Query-Driven Topic Model (2021.findings-acl)

Challenge:	Existing models for user reviews are limited by data sparsity and lack of data.
Approach:	They propose to integrate LSTM and Topic Modeling to extract review information for recommender systems by utilizing user reviews.
Outcome:	The proposed model outperforms existing models on Amazon review dataset and shows better ability on making topic clustering than traditional topic model based method.

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Zheng Fang, Yulan He, Rob Procter

TopicGPT: A Prompt-based Topic Modeling Framework (2024.naacl-long)

Challenge:	Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus.
Approach:	They propose a query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics.
Outcome:	The proposed model is particularly attractive when the query has a low occurrence in a text corpus, making it difficult for traditional topic models to identify relevant topics.

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Chau Pham, Alexander Hoyle, Simeng Sun, Philip Resnik, Mohit Iyyer

Studying the Evolution of Scientific Topics and their Relationships (2021.findings-acl)

Challenge:	TopicGPT uses large language models to uncover latent topics in text . topic models represent topics as bags of words that require "reading the tea leaves" topic models also offer limited control over formatting and specificity of topics .
Approach:	TopicGPT uses large language models to uncover latent topics in text . authors propose a prompt-based framework that produces topics that align better with human categorizations .
Outcome:	TopicGPT produces topics that align better with human categorizations compared to competing methods.

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Ana Sabina Uban, Cornelia Caragea, Liviu P. Dinu

Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models (2025.coling-main)

Challenge:	a study of scientific topics and their evolution through time is proposed . we analyze scientific texts published in the field of computational linguistics .
Approach:	They propose a multidimensional approach to studying scientific topics through time and their relationships between them.
Outcome:	The proposed model analyzes scientific texts published in the ACL Anthology and compares them with case studies to understand how topics evolve and disappear over time.

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Benjamin Icard, Evangelia Zve, Lila Sainero, Alice Breton, Jean-Gabriel Ganascia

Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling (2021.naacl-main)

Challenge:	Using a literary corpus that alternates between topics and styles, we compare language models across French and English.
Approach:	They analyze how writing style affects embedding spaces across multiple language models . they use a literary corpus that alternates between topics and styles to compare their results .
Outcome:	The proposed model is based on two established literary works in French and English.

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Aaron Mueller, Mark Dredze

Enhancing Air Quality Prediction with Social Media and Natural Language Processing (P19-1)

Challenge:	Topic models can augment or replace bag-of-words inputs with pre-trained transformer-based word prediction models.
Approach:	They propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual topic modeling.
Outcome:	The proposed methods improve both monolingual and zero-shot polylingual topic modeling.

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Jyun-Yu Jiang, Xue Sun, Wei Wang, Sean Young

A Term Extraction Approach to Survey Analysis in Health Care (2020.lrec-1)

Challenge:	predicting air quality is a major concern for human health, but the changes of air quality conditions are still difficult to monitor.
Approach:	They propose to exploit social media and natural language processing techniques to enhance air quality prediction.
Outcome:	The proposed approach improves air quality prediction over baseline that does not use social media by 6.9% to 17.7% in macro-F1 scores.

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Cécile Robin, Mona Isazad Mashinchi, Fatemeh Ahmadi Zeleti, Adegboyega Ojo, Paul Buitelaar

Reinforcement Learning for Topic Models (2023.findings-acl)

Challenge:	a new study examines the impact of customer feedback on health care organizations . the results of the 2017 Irish National Inpatient Survey are compared to a manual framework .
Approach:	They propose an approach to patient experience using free text questions from the 2017 Irish National Inpatient Survey campaign.
Outcome:	The proposed approach to patient experience is based on the results of the 2017 Irish National Inpatient Survey.

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Jeremy Costello, Marek Reformat

Twitter Topic Classification (2022.coling-1)

Challenge:	Topic modeling is a method to extract information from documents by grouping topics into topics and linking them with words describing them.
Approach:	They propose to replace the variational autoencoder with a continuous action space reinforcement learning policy and modify the neural network architecture to weight the ELBO loss.
Outcome:	The proposed model outperforms all other unsupervised models and performs on par with or better than most models using supervised labeling and contrastive learning.

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Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados, Vitor Silva, Leonardo Neves, Francesco Barbieri

Restoring and Mining the Records of the Joseon Dynasty via Neural Language Modeling and Machine Translation (2021.naacl-main)

Challenge:	Existing methods to identify topics from posts are difficult to interpret and can differ from corpus to corpus.
Approach:	They propose a task based on tweet topic classification and release two datasets that can be used to train and test models.
Outcome:	The proposed task is based on two datasets from recent time periods and provides training and testing data.

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Kyeongpil Kang, Kyohoon Jin, Soyoung Yang, Soojin Jang, Jaegul Choo, Youngbin Kim

Machine Translation

Challenge:	voluminous historical records are difficult to fully utilize since they are written in ancient languages and some parts are damaged over time.
Approach:	They propose a multi-task learning approach to restore and translate historical documents using a self-attention mechanism.
Outcome:	The proposed approach improves the accuracy of the translation task over baselines without multi-task learning.

Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis (2026.findings-acl)

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Xuan Xu, Zhongliang Yang, Haolun Li, Rui Tian, Beilin Chu, J Song, Yu Li, Shaolin Tan, Linna Zhou

Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables (2022.emnlp-main)

Challenge:	Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing.
Approach:	They propose a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows.
Outcome:	The proposed taxonomy emphasizes the role of LLMs and the design of end-to-end workflows.

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Erxin Yu, Lan Du, Yuan Jin, Zhepei Wei, Yi Chang

Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature (2022.coling-1)

Challenge:	Recent discrete latent variable models have received a surge of interest in both NLP and CV . they are comparable to the continuous counterparts in representation learning, but are more interpretable in their predictions.
Approach:	They develop a topic-informed discrete latent variable model for semantic textual similarity . they inject the quantized representation into a transformer-based language model .
Outcome:	The proposed model outperforms strong baselines in semantic textual similarity tasks.

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Sajawel Ahmed, Rob van der Goot, Misbahur Rehman, Carl Kruse, Ömer Özsoy, Alexander Mehler, Gemma Roig

GINopic: Topic Modeling with Graph Isomorphism Network (2024.naacl-long)

Challenge:	Named entity recognition and topic modeling are crucial for downstream tasks in natural language processing.
Approach:	They propose to address named entity recognition and topic modeling on CA literature . they manually annotate the work of Tafsir Al-Tabari with span-based NEs .
Outcome:	The results show that the proposed task can perform state-of-the-art on historical topic models.

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Suman Adhya, Debarshi Sanyal

Creation and Analysis of an International Corpus of Privacy Laws (2024.lrec-main)

Challenge:	Recent studies focus on the representation of documents as a sequence of words, but word dependency patterns are not captured in topic modeling.
Approach:	They propose a topic modeling framework based on graph isomorphism networks to capture word dependencies between words.
Outcome:	The proposed framework is compared with existing topic models on a dataset of a large text collection and shows that it can uncover the underlying topics in an unsupervised manner.

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Sonu Gupta, Geetika Gopi, Harish Balaji, Ellen Poplavska, Nora O’Toole, Siddhant Arora, Thomas Norton, Norman Sadeh, Shomir Wilson

Benchmarking Neural Topic Models: An Empirical Study (2021.findings-acl)

Challenge:	a corpus of 1,043 privacy laws, regulations, and guidelines covers 183 jurisdictions . prior efforts to study privacy law in the form of privacy policies have lacked a large-scale collection .
Approach:	They propose a corpus of 1,043 privacy laws, regulations, and guidelines covering 183 jurisdictions.
Outcome:	The Privacy Law Corpus covers 1,043 privacy laws, regulations, and guidelines covering 183 jurisdictions.

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Thanh-Nam Doan, Tuan-Anh Hoang

Are Neural Topic Models Broken? (2022.findings-emnlp)

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.

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Alexander Miserlis Hoyle, Pranav Goel, Rupak Sarkar, Philip Resnik

TeCoFeS: Text Column Featurization using Semantic Analysis (2025.findings-naacl)

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.

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Ananya Singha, Mukul Singh, Ashish Tiwari, Sumit Gulwani, Vu Le, Chris Parnin

Challenge:	Existing methods for topic modeling and feature extraction are based on syntactic features and overlook the semantics.
Approach:	They propose a semantic text column featurization problem that extracts a small sample smartly using an LLM to label only the sample and then extends that labeling to the whole column using text embeddings.
Outcome:	The proposed approach performs better than baselines and naive use of LLMs.

Topic Modeling for Short Texts via Optimal Transport-Based Clustering (2025.findings-acl)

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Tu Vu, Manh Do, Tung Nguyen, Linh Ngo Van, Sang Dinh, Thien Huu Nguyen

Pre-training and Fine-tuning Neural Topic Model: A Simple yet Effective Approach to Incorporating External Knowledge (2022.acl-long)

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.

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Linhai Zhang, Xuemeng Hu, Boyu Wang, Deyu Zhou, Qian-Wen Zhang, Yunbo Cao

Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy (2025.findings-naacl)

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.

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Tunazzina Islam, Dan Goldwasser

Challenge:	Supervised methods are adept at text categorization, but dynamic nature of social media debates pose challenges for them . traditional methods for extracting themes from public discourse often reveal overarching patterns that might not capture specific nuances.
Approach:	They propose a generic approach that leverages the advanced capabilities of Large Language Models to extract latent arguments from social media messaging.
Outcome:	The proposed approach leverages the advanced capabilities of Large Language Models (LLMs) to extract latent arguments from social media messaging.

Through the Lens of History: Methods for Analyzing Temporal Variation in Content and Framing of State-run Chinese Newspapers (2025.naacl-long)

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Shijia Liu, David A. Smith

Sentiment Analysis

Challenge:	State-run newspapers are believed to strategically select and frame news articles to align with the shifting political tides of the country.
Approach:	They analyze more than 50 years of articles from the People's Daily and Reference News to quantify differences in content and framing over time.
Outcome:	The proposed methods show that the changes in name mentions and sentiment in news articles are more significant in People’s Daily than in Reference News .

Auto-Encoding Variational Bayes for Inferring Topics and Visualization (2020.coling-main)

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Dang Pham, Tuan Le

Moontae Lee, Sungjun Cho, David Bindel, David Mimno

Challenge:	Visualization and topic modeling are widely used for text analysis, but there is a lack of semantic interpretation as they do not extract topics.
Approach:	They propose a fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization using a single generative model.
Outcome:	The proposed method can handle model changes efficiently with little rederivation effort.

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Multilingual Corpus Creation for Multilingual Semantic Similarity Task (2020.lrec-1)

Challenge:	spectral topic models lack reliability in real data and lack of practical implementations.
Approach:	They propose to use a spectral topic inference method to infer correlations between topics in real data and a matrix-based approach to inference.
Outcome:	The proposed method outperforms tensor-based methods and probabilistic methods in real data and provides a complete guide to correlated topic modeling.

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Mahtab Ahmed, Chahna Dixit, Robert E. Mercer, Atif Khan, Muhammad Rifayat Samee, Felipe Urra

Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue (2024.lrec-main)

Challenge:	Existing monolingual corpora are limited for semantic similarity tasks . a major factor affecting the success of deep neural networks is the availability of large and good quality corpors.
Approach:	They propose a semi-automated framework to create a multilingual corpus for a semantic similarity task.
Outcome:	The proposed framework can be applied to government, insurance, banking domains provided a bilingual website exists.

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Nhat Tran, Diane Litman

Natural Language Interfaces

Challenge:	Existing approaches to knowledge retrieval are limited by the knowledge base encoder, but our work focuses on the knowledge-base encoder.
Approach:	They propose an approach that utilizes topic modeling on the knowledge base to improve retrieval accuracy and as a result, improve response generation.
Outcome:	The proposed approach can improve retrieval and generation performance on two datasets.

RoBERT2VecTM: A Novel Approach for Topic Extraction in Islamic Studies (2024.findings-emnlp)

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Sania Aftar, Luca Gagliardelli, Amina Ganadi, Federico Ruozzi, Sonia Bergamaschi

Mining Crowdsourcing Problems from Discussion Forums of Workers (2020.coling-main)

Challenge:	a new approach to investigate “Hadith” texts presents challenges due to the complexity of Arabic . a novel neural-based approach to analyze “Matn” topics outperforms traditional NLP models .
Approach:	They propose a novel approach to analyze Arabic “Hadith” texts using the Contextualized Topic Model.
Outcome:	The proposed approach outperforms state-of-the-art models by generating more coherent topics in Arabic.

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Zahra Nouri, Henning Wachsmuth, Gregor Engels

Is Language Modeling Enough? Evaluating Effective Embedding Combinations (2020.lrec-1)

Challenge:	Among the most widely used platforms are Upwork, Appen, and above all Amazon Mechanical Turk (MTurk) which host annotation tasks and collect huge sets of annotated data from workers.
Approach:	They propose to use topic modeling to analyze workers' complaints from a new English corpus of workers’ forum discussions to identify problems in task design, task operation, and task evaluation that workers face with requesters in crowdsourcing processes.
Outcome:	The findings form the basis for future research on how to improve crowdsourcing processes.

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Rudolf Schneider, Tom Oberhauser, Paul Grundmann, Felix Alexander Gers, Alexander Loeser, Steffen Staab

Exploring Neural Topic Modeling on a Classical Latin Corpus (2024.lrec-main)

Challenge:	specialized embeddings are not available for tasks like entity linking or paragraph classification.
Approach:	They evaluate whether universal embeddings can be complemented by specialized embeddables.
Outcome:	The proposed embeddings outperform state-of-the-art embeddables without any fine-tuning.

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Ginevra Martinelli, Paola Impicciché, Elisabetta Fersini, Francesco Mambrini, Marco Passarotti

Platform-Invariant Topic Modeling via Contrastive Learning to Mitigate Platform-Induced Bias (2024.findings-emnlp)

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.

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Minseo Koo, Doeun Kim, Sungwon Han, Sungkyu Park

FoTo: Targeted Visual Topic Modeling for Focused Analysis of Short Texts (2024.lrec-main)

Challenge:	Existing topic models that analyze documents from multiple platforms are not able to capture the authentic topics due to platform-induced biases.
Approach:	They propose to use a platform-invariant contrastive learning algorithm to reduce platform influence in topic models by removing platform-specific jargon word sets.
Outcome:	The proposed model reduces platform influence in topic models by developing a platform-invariant contrastive learning algorithm and removing platform-specific jargon word sets.

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Sanuj Kumar, Tuan Le

A Bayesian Topic Model for Human-Evaluated Interpretability (2022.lrec-1)

Challenge:	Existing topic models perform a full corpus analysis that treats all topics equally, making them not on target.
Approach:	They propose a targeted topic model for semantic short-text embedding which aims to learn all topics and low-dimensional visual representations of documents while preserving relevant topics.
Outcome:	The proposed model learns all topics and low-dimensional visual representations while preserving relevant topics in the visualization space.

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Justin Wood, Corey Arnold, Wei Wang

Topic-Guided Self-Introduction Generation for Social Media Users (2023.findings-acl)

Challenge:	Topic modeling is an effective way to analyze unstructured textual data.
Approach:	They propose to combine nonparametric and weakly-supervised topic models to produce interpretable topics.
Outcome:	The proposed model outperforms weakly-supervised models in the field of topic modeling.

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Chunpu Xu, Jing Li, Piji Li, Min Yang

Language Models

Challenge:	Existing studies on social media use tags to profile users, but we have found that sentence-level self-introductions are more natural and engaging.
Approach:	They propose a novel topic-guided encoder-decoder framework that uses a user's tweeting history to generate a short sentence outlining their personal interests.
Outcome:	The proposed framework outperforms existing encoder-decoder models on a large-scale Twitter dataset and shows that it is more natural and engaging than previous approaches.

CluHTM - Semantic Hierarchical Topic Modeling based on CluWords (2020.acl-main)

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Felipe Viegas, Washington Cunha, Christian Gomes, Antônio Pereira, Leonardo Rocha, Marcos Goncalves

Improving Embeddings Representations for Comparing Higher Education Curricula: A Use Case in Computing (2022.emnlp-main)

Challenge:	Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration.
Approach:	They propose a hierarchical matrix factorization that exploits latent topics and relationships among them to create a powerful tool for data analysis and exploration.
Outcome:	The proposed method outperforms baselines and datasets in the vast majority of cases.

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Jeffri Murrugarra-Llerena, Fernando Alva-Manchego, Nils Murrugarra-LLerena

Representation Learning

Challenge:	Existing methods to represent study programs using bag-of-words and clustering algorithms are prone to biases due to personal beliefs and perspectives.
Approach:	They propose to use pre-trained word embeddings to fine-tune a study program classification task to obtain more accurate curriculum representations than strong baselines.
Outcome:	The proposed method is compared to existing methods on a study program classification task and on comparing computing curricula from USA and Latin America.

Topic Modeling: Contextual Token Embeddings Are All You Need (2024.findings-emnlp)

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Dimo Angelov, Diana Inkpen

Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling (2023.acl-long)

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.

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Shengqiong Wu, Hao Fei, Yixin Cao, Lidong Bing, Tat-Seng Chua

Multimodality

Challenge:	Existing research on multimodal relation extraction (MRE) faces internal-information over-utilization and external-information under-exploitation.
Approach:	They propose a framework that implements internal-information screening and external-information exploiting to address these challenges.
Outcome:	The proposed framework outperforms the current best model on the benchmark dataset.

Two Huge Title and Keyword Generation Corpora of Research Articles (2020.lrec-1)

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Erion Çano, Ondřej Bojar

Text Generation

Challenge:	Recent advances in sequence-to-sequence learning with neural networks have improved the quality of automatically generated text summaries and document keywords.
Approach:	They propose to use OAGSX and OAGKX datasets to analyze text summaries and document keywords.
Outcome:	The proposed models perform better than previous models on two large datasets . the authors hope to use the results to derive subsets of research articles from more disciplines .

TopicNet: Making Additive Regularisation for Topic Modelling Accessible (2020.lrec-1)

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Victor Bulatov, Vasiliy Alekseev, Konstantin Vorontsov, Darya Polyudova, Eugenia Veselova, Alexey Goncharov, Evgeny Egorov

Open-world Multi-label Text Classification with Extremely Weak Supervision (2024.emnlp-main)

Challenge:	TopicNet is a Python module for topic modeling.
Approach:	They introduce a Python module for topic modeling that brings regularization topic modeling to non-specialists using a general-purpose language.
Outcome:	The proposed module aims to bring topic modeling to non-specialists using a general-purpose language.

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Xintong Li, Jinya Jiang, Ria Dharmani, Jayanth Srinivasa, Gaowen Liu, Jingbo Shang

Labeling Results of Topic Models: Word Sense Disambiguation as Key Method for Automatic Topic Labeling with GermaNet (2024.lrec-main)

Challenge:	Similar single-label XWS settings cannot be easily adapted for multi-l label classification.
Approach:	They propose a novel method for open-world multi-label text classification under extremely weak supervision where the user provides a brief description without any labels or ground-truth label space.
Outcome:	The proposed method exhibits a remarkable increase in ground-truth label space coverage on various datasets.

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Jennifer Ecker

LLM-Guided Semantic-Aware Clustering for Topic Modeling (2025.acl-long)

Challenge:	Existing methods for topic modeling are not suitable for document classification, but they can be used to generate training data from large corpus.
Approach:	They propose to use topic modeling and automatic topic labeling to understand large corpora of text.
Outcome:	The proposed method is more accurate than existing methods.

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Jianghan Liu, Ziyu Shang, Wenjun Ke, Peng Wang, Zhizhao Luo, Jiajun Liu, Guozheng Li, Yining Li

Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs (2024.findings-emnlp)

Challenge:	Experimental results show that topic modeling is competitive compared to closed-source methods.
Approach:	They propose a semi-supervised topic modeling method that combines LLMs with clustering to improve topic generation and distribution.
Outcome:	The proposed method outperforms state-of-the-art methods that utilize GPT-4 on topic alignment and exhibits competitive performance compared to Neural Topic Models on topic quality.

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Pritom Saha Akash, Kevin Chang

S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling (2025.acl-long)

Challenge:	Existing topic models often lack sufficient word co-occurrence in short texts, resulting in incoherent topics.
Approach:	They propose to use large language models to extend short texts into more detailed sequences before applying topic modeling to solve semantic inconsistency problem.
Outcome:	The proposed approach significantly outperforms current state-of-the-art topic models on real-world datasets with extreme data sparsity.

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Suman Adhya, Debarshi Kumar Sanyal

Tell, Don’t Show: Leveraging Language Models’ Abstractive Retellings to Model Literary Themes (2025.findings-acl)

Challenge:	Variational autoencoder-based neural topic models lack posterior collapse in high-dimensional text data.
Approach:	They propose a Spherical Sliced Wasserstein Autoencoder for Topic Modeling to model latent representations in a hyperspherical space.
Outcome:	The proposed model outperforms state-of-the-art topic models while improving performance on downstream tasks.

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Li Lucy, Camilla Griffiths, Sarah Levine, Jennifer L Eberhardt, Dorottya Demszky, David Bamman

Language Models

Challenge:	Literature challenges traditional bag-of-words approaches for topic modeling because narrative language focuses on immersive sensory details instead of abstractive description or exposition.
Approach:	They propose a topic modeling approach that prompts generative language models to tell what passages show, thereby translating narratives’ surface forms into higher-level concepts and themes.
Outcome:	The proposed model can translate narratives’ surface forms into higher-level concepts and themes than by running LDA alone or directly asking LMs to list topics.

From Documents to Segments: A Contextual Reformulation for Topic Assignment (2026.findings-acl)

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Hoonsang Yoon, Takyoung Kim, Wonkee Lee, Ilmin Cho, Dilek Hakkani-Tür, Stanley Jungkyu Choi

Explainability & Interpretability in NLP

Challenge:	Traditional topic modeling treats each document as a single, coherent unit of topic.
Approach:	They propose a paradigm that redefines topic assignment at the level of segments . they propose 'segment intrusion task' to extend word intrusion to the span level .
Outcome:	The proposed paradigm improves topic purity, interpretability and applicability to multi-theme corpora.

The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations (2026.acl-long)

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Pierre-Antoine Lequeu, Léo Labat, Laurène Cave, Gaël Lejeune, François Yvon, Benjamin Piwowarski

Challenge:	Large Language Models (LLMs) are ubiquitous in modern NLP, but ethical questions have been raised about their use as analysis tools.
Approach:	They propose a framework that transforms noisy, multi-topic contributions into argumentative units ready for downstream analysis.
Outcome:	The proposed framework can be run locally and transparently with limited resources.

HICode: Hierarchical Inductive Coding with LLMs (2025.emnlp-main)

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Mian Zhong, Pristina Wang, Anjalie Field

Challenge:	Existing methods for fine-grained corpus analysis are limited to small subsets and require manual labeling and statistical tools like topic modeling.
Approach:	They propose a pipeline that inductively generates labels from analysis data and then hierarchically clusters them to surface emergent themes.
Outcome:	The proposed pipeline validates the approach across three datasets and shows it is robust through automated and human evaluations.

CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling (2026.findings-acl)

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Karthik Singaravadivelan, Anant Gupta, Zekun Wang, Christopher J. MacLellan