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Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications

2019-06-06 21:53:53
Wei Zhao, Haiyun Peng, Steffen Eger, Erik Cambria, Min Yang

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: 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.

Abstract (translated)

阻碍胶囊网络发展以挑战NLP应用的障碍包括对大输出空间的不可扩展性和不太可靠的路由过程。在本文中,我们介绍了:1)在实例级评估路由过程性能的协议评分;2)增强路由可靠性的自适应优化器;3)胶囊压缩和部分路由以提高胶囊网络的可扩展性。我们在两个NLP任务上验证了我们的方法,即:多标签文本分类和问题解答。实验结果表明,在这两项任务上,我们的方法比强大的竞争对手有了很大的改进。此外,我们在低资源环境中获得最佳结果,培训实例很少。

URL

https://arxiv.org/abs/1906.02829

PDF

https://arxiv.org/pdf/1906.02829.pdf


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