Differentiable Reasoning on Large Knowledge Bases and Natural Language

Understand

Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering.

• General neural architectures that jointly learn representations and transformations of text are very data-inefficient, and it is hard to analyse their reasoning process.
• These issues are addressed by end-to-end differentiable reasoning systems such as Neural Theorem Provers (NTPs), although they can only be used with small-scale symbolic KBs.
• In this paper we first propose Greedy NTPs (GNTPs), an extension to NTPs addressing their complexity and scalability limitations, thus making them applicable to real-world datasets.