Towards Scalable and Reliable Capsule Networksfor Challenging NLP Applications
Table 2: Comparisons of our NLP-Cap approach and baselines on two text classification benchmarks, where ’-’ denotes methods that failed to scale due to memory issues.
['<bold>Datasets</bold>', '<bold>Metrics</bold>', '<bold>FastXML</bold>', '<bold>PD-Sparse</bold>', '<bold>FastText</bold>', '<bold>Bow-CNN</bold>', '<bold>CNN-Kim</bold>', '<bold>XML-CNN</bold>', '<bold>Cap-Zhao</bold>', '<bold>NLP-Cap</bold>', '<bold>Impv</bold>']
In Table 2, we can see a noticeable margin brought by our capsule-based approach over the strong baselines on EUR-Lex, and competitive results on RCV1. These results appear to indicate that our approach has superior generalization ability on datasets with fewer training examples, i.e., RCV1 has 729.67 examples per label while EUR-Lex has 15.59 examples.