# Coordinated Topic Modeling

arXiv: https://arxiv.org/abs/2210.08559 · HTML: https://mlsanity.com/paper/2210.08559/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Weakly-Supervised Neural Text Classification](https://mlsanity.com/paper/1809.01478/) (2018)
- [Discriminative Topic Mining via Category-Name Guided Text Embedding](https://mlsanity.com/paper/1908.07162/) (2019)
- [Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge](https://mlsanity.com/paper/1611.10277/) (2016)
- [Automatic Labelling of Topics with Neural Embeddings](https://mlsanity.com/paper/1612.05340/) (2016)
- [Keyword Assisted Topic Models](https://mlsanity.com/paper/2004.05964/) (2020)
- [Keyword Assisted Embedded Topic Model](https://mlsanity.com/paper/2112.03101/) (2021)
- [Towards Autoencoding Variational Inference for Aspect-based Opinion Summary](https://mlsanity.com/paper/1902.02507/) (2019)

## Built on by (most cited first, 2 of 2)

- [TopicAdapt- An Inter-Corpora Topics Adaptation Approach](https://mlsanity.com/paper/2310.04978/) (2023)
- [DiffETM: Diffusion Process Enhanced Embedded Topic Model](https://mlsanity.com/paper/2501.00862/) (2025)

## Abstract (arXiv)

We propose a new problem called coordinated topic modeling that imitates human behavior while describing a text corpus. It considers a set of well-defined topics like the axes of a semantic space with a reference representation. It then uses the axes to model a corpus for easily understandable representation. This new task helps represent a corpus more interpretably by reusing existing knowledge and benefits the corpora comparison task. We design ECTM, an embedding-based coordinated topic model that effectively uses the reference representation to capture the target corpus-specific aspects while maintaining each topic's global semantics. In ECTM, we introduce the topic- and document-level supervision with a self-training mechanism to solve the problem. Finally, extensive experiments on multiple domains show the superiority of our model over other baselines.