Knowledge Augmented Inference Network for Natural Language Inference

This paper proposes a Knowledge Augmented Inference Network (K- AIN) that can effectively incorporate external knowledge into existing neural network models on Natural Language Inference (NLI) task. Different from previous works that use one-hot representations to describe external knowledge, we employ the TransE model to encode various semantic relations extracted from the external Knowledge Base (KB) as distributed relation features. We utilize these distributed relation features to construct knowledge augmented word embeddings and integrate them into the current neural network models. Experimental results show that our model achieves a better performance than the strong baseline on the SNLI dataset and we also surpass the current state-of-the-art models on the SciTail dataset.