Understanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification (original) (raw)

Frequently, Text Classification is limited by insufficient training data. This problem is addressed by ZeroShot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance.