| Challenge: | Existing classification models for short texts are weak due to data sparsity . |
| Approach: | They propose topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels. |
| Outcome: | The proposed model outperforms state-of-the-art models on short text classification, while generating coherent topics. |
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Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder (2020.emnlp-main)
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| Challenge: | Topic models for short texts suffer from data sparsity because of limited word co-occurrences. |
| Approach: | They propose a neural topic model with a new topic distribution quantization approach that generates peakier distributions that are more appropriate for modeling short texts. |
| Outcome: | The proposed model outperforms both strong traditional and neural baselines under extreme data sparsity scenes, producing high-quality topics. |
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. |
Topic Modeling for Short Texts with Large Language Models (2024.acl-srw)
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| 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. |
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning (2022.emnlp-main)
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| Challenge: | Existing topic models do not make full use of word co-occurrence information to model latent topics. |
| Approach: | They propose a novel short text topic modeling framework, Topic-Semantic Contrastive Topic Model, which uses augmented data and the data characteristic to learn the relations among samples. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines regardless of the data augmentation availability, producing high-quality topics and topic distributions. |
GloCOM: A Short Text Neural Topic Model via Global Clustering Context (2025.naacl-long)
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| Challenge: | Existing neural topic models often overlook uncovering hidden topics from short texts due to data sparsity, poor aggregation quality, and difficulty in inferring topic proportions for individual documents. |
| Approach: | They propose a model which constructs global clustering contexts for short texts using text embeddings from pre-trained language models. |
| Outcome: | The proposed model outperforms state-of-the-art models on short texts in topic quality and document representation. |
Simple Attention-Based Representation Learning for Ranking Short Social Media Posts (N19-1)
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| Challenge: | Existing approaches to ranking short social media posts are complex and require different components to capture a multitude of relevance signals. |
| Approach: | They propose a word-level Siamese architecture with attention-based mechanisms for capturing semantic "soft" matches between query and post tokens. |
| Outcome: | The proposed model is faster and simpler than existing models and more efficient than existing approaches. |
Simplified Graph Learning for Inductive Short Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods for short text classification are limited and lack of labeled data is not enough. |
| Approach: | They propose a novel short text classification algorithm which leverages words to handle the lack of labeled data. |
| Outcome: | The proposed model performs better with lower memory consumption and faster inference speed than previous models. |
Dynamic Memory Induction Networks for Few-Shot Text Classification (2020.acl-main)
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| Challenge: | Recent studies have shown that models can benefit from query-aware methods for few-shot text classification. |
| Approach: | They propose a dynamic memory-based network for few-short text classification that uses static memory to adapt to unseen classes. |
| Outcome: | The proposed model improves on the miniRCV1 and ODIC datasets by 24% . Detailed analysis is performed to show how the proposed network achieves the new performance. |
Combating Label Sparsity in Short Text Topic Modeling via Nearest Neighbor Augmentation (2024.findings-acl)
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| Challenge: | Existing topic models suffer from poor performance when applied to short text contents due to the limited length of a single topic. |
| Approach: | They propose a neural short text topic model that augments reconstruction labels with k-nearest documents to complement relevant but unobserved words. |
| Outcome: | The proposed model outperforms the state-of-the-art models on multiple public short-text datasets and can derive high-quality topics and document representations. |
The Strength of the Weakest Supervision: Topic Classification Using Class Labels (N19-3)
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| Challenge: | a topic classifier can understand only class labels when training for tasks that require a large amount of labeled documents. |
| Approach: | They propose an algorithm that can initialize a topic classifier using only class labels . they propose a method that combines word embedding and naive Bayes classification . |
| Outcome: | The proposed approach saves significant initial labeling effort by providing a "warm start" the proposed approach can be fine-tuned with more labeled documents to reach a certain performance level. |