# Topic Memory Networks for Short Text Classification

arXiv: https://arxiv.org/abs/1809.03664 · HTML: https://mlsanity.com/paper/1809.03664/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 25 of 25)

- [Cross-modal Memory Networks for Radiology Report Generation](https://mlsanity.com/paper/2204.13258/) (2022)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Enriching and Controlling Global Semantics for Text Summarization](https://mlsanity.com/paper/2109.10616/) (2021)
- [Topic-Guided Abstractive Multi-Document Summarization](https://mlsanity.com/paper/2110.11207/) (2021)
- [A Survey on Neural Topic Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2401.15351/) (2024)
- [Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling](https://mlsanity.com/paper/2305.11719/) (2023)
- [Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification](https://mlsanity.com/paper/2111.00180/) (2021)
- [VIBE: Topic-Driven Temporal Adaptation for Twitter Classification](https://mlsanity.com/paper/2310.10191/) (2023)
- [Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations](https://mlsanity.com/paper/2106.09896/) (2021)
- [Out-of-Town Recommendation with Travel Intention Modeling](https://mlsanity.com/paper/2101.12555/) (2021)
- [Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge](https://mlsanity.com/paper/2408.09422/) (2024)
- [Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs](https://mlsanity.com/paper/2410.03071/) (2024)
- [GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction](https://mlsanity.com/paper/2205.03786/) (2022)
- [Time Will Change Things: An Empirical Study on Dynamic Language Understanding in Social Media Classification](https://mlsanity.com/paper/2210.02857/) (2022)
- [Towards Better Multi-modal Keyphrase Generation via Visual Entity Enhancement and Multi-granularity Image Noise Filtering](https://mlsanity.com/paper/2309.04734/) (2023)
- [CoT-Driven Framework for Short Text Classification: Enhancing and Transferring Capabilities from Large to Smaller Model](https://mlsanity.com/paper/2401.03158/) (2024)
- [Generative Deduplication For Socia Media Data Selection](https://mlsanity.com/paper/2401.05883/) (2024)
- [UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization](https://mlsanity.com/paper/2306.06851/) (2023)
- [Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management](https://mlsanity.com/paper/2103.11835/) (2021)
- [A Smart and Defensive Human-Machine Approach to Code Analysis](https://mlsanity.com/paper/2108.03294/) (2021)
- [Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables](https://mlsanity.com/paper/2211.03616/) (2022)
- [HICL: Hashtag-Driven In-Context Learning for Social Media Natural Language Understanding](https://mlsanity.com/paper/2308.09985/) (2023)
- [Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling](https://mlsanity.com/paper/2310.15420/) (2023)
- [Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models](https://mlsanity.com/paper/2510.09358/) (2025)
- [WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection](https://mlsanity.com/paper/2607.04350/) (2026)

## Abstract (arXiv)

Many classification models work poorly on short texts due to data sparsity. To address this issue, we propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. Different from most prior work that focuses on extending features with external knowledge or pre-trained topics, our model jointly explores topic inference and text classification with memory networks in an end-to-end manner. Experimental results on four benchmark datasets show that our model outperforms state-of-the-art models on short text classification, meanwhile generates coherent topics.