@inproceedings{zhao-etal-2019-towards,
    title = "Towards Scalable and Reliable Capsule Networks for Challenging {NLP} Applications",
    author = "Zhao, Wei  and
      Peng, Haiyun  and
      Eger, Steffen  and
      Cambria, Erik  and
      Yang, Min",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/P19-1150/",
    doi = "10.18653/v1/P19-1150",
    pages = "1549--1559",
    abstract = "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: (i) an agreement score to evaluate the performance of routing processes at instance-level; (ii) an adaptive optimizer to enhance the reliability of routing; (iii) 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."
}