# Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

arXiv: https://arxiv.org/abs/1906.02829 · HTML: https://mlsanity.com/paper/1906.02829/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 10 of 10)

- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks](https://mlsanity.com/paper/2112.03271/) (2021)
- [Automatic Text Evaluation through the Lens of Wasserstein Barycenters](https://mlsanity.com/paper/2108.12463/) (2021)
- [Learning with Capsules: A Survey](https://mlsanity.com/paper/2206.02664/) (2022)
- [Graph Routing between Capsules](https://mlsanity.com/paper/2106.11531/) (2021)
- [Recent, rapid advancement in visual question answering architecture: a review](https://mlsanity.com/paper/2203.01322/) (2022)
- [Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis](https://mlsanity.com/paper/2306.07845/) (2023)
- [Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation Learning](https://mlsanity.com/paper/2206.13465/) (2022)
- [Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension](https://mlsanity.com/paper/2208.08750/) (2022)
- [Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques](https://mlsanity.com/paper/2312.00053/) (2023)

## Abstract (arXiv)

Obstacles hindering the development of capsule networks for challenging NLP applications include poor scalability to large output spaces and less reliable routing processes. In this paper, we introduce: 1) an agreement score to evaluate the performance of routing processes at instance level; 2) an adaptive optimizer to enhance the reliability of routing; 3) capsule compression and partial routing to improve the scalability of capsule networks. We validate our approach on two NLP tasks, namely: multi-label text classification and question answering. Experimental results show that our approach considerably improves over strong competitors on both tasks. In addition, we gain the best results in low-resource settings with few training instances.