Proceedings of the Annual Meeting of the Cognitive Science Society

An important challenge for human-like AI is compositional se-mantics. Recent research has attempted to address this by us-ing deep neural networks to learn vector space embeddings ofsentences, which then serve as input to other tasks. We presenta new dataset for one such task, “natural language inference”(NLI), that cannot be solved using only word-level knowledgeand requires some compositionality. We find that the perfor-mance of state of the art sentence embeddings (InferSent; Con-neau et al., 2017) on our new dataset is poor. We analyzethe decision rules learned by InferSent and find that they arelargely driven by simple heuristics that are ecologically validin its training dataset. Further, we find that augmenting train-ing with our dataset improves test performance on our datasetwithout loss of performance on the original training dataset.This highlights the importance of structured datasets in betterunderstanding and improving AI systems.