Can we explain natural language inference decisions taken with neural networks? Inference rules in distributed representations

Natural Language Inference (NLI) is a key, complex task where machine learning (ML) is playing an important role. However, ML has progressively obfuscated the role of linguistically-motivated inference rules, which should be the core of NLI systems. In this paper, we introduce distributed inference rules as a novel way to encode linguistically-motivated inference rules in learning interpretable NLI classifiers. We propose two encoders: the Distributed Partial Tree Encoder and the Distributed Smoothed Partial Tree Encoder. These encoders allow modeling syntactic and syntactic-semantic inference rules as distributed representations ready to be used in ML models over large datasets. Although far from the state-of-the-art of end-to-end deep learning systems on large datasets, our shallow networks positively exploit inference rules for NLI, improving over baseline systems. This is a first positive step towards interpretable and explainable end-to-end deep learning systems