Short Text Clustering with Transformers
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Abstract (arXiv)
Recent techniques for the task of short text clustering often rely on word embeddings as a transfer learning component. This paper shows that sentence vector representations from Transformers in conjunction with different clustering methods can be successfully applied to address the task. Furthermore, we demonstrate that the algorithm of enhancement of clustering via iterative classification can further improve initial clustering performance with different classifiers, including those based on pre-trained Transformer language models.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 31 Jan 2021.
Builds on
10 of 21 references.
References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2018Universal Sentence Encodercited by 485
- 2019Multilingual Universal Sentence Encoder for Semantic Retrievalcited by 192
- 2019Short Text Topic Modeling Techniques, Applications, and Performance: A Surveycited by 38
- 2017Self-Taught Convolutional Neural Networks for Short Text Clusteringcited by 34
- 2018Improving Topic Models with Latent Feature Word Representationscited by 28
- 2015Text Understanding from Scratchcited by 25
- 2020Enhancement of Short Text Clustering by Iterative Classificationcited by 16
- 2019Attentive Representation Learning with Adversarial Training for Short Text Clusteringcited by 2
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2.
3 papers.