Multi-hop Question Answering via Reasoning Chains

Understand

Multi-hop question answering requires models to gather information from different parts of a text to answer a question.

• Most current approaches learn to address this task in an end-to-end way with neural networks, without maintaining an explicit representation of the reasoning process.
• We propose a method to extract a discrete reasoning chain over the text, which consists of a series of sentences leading to the answer.
• We then feed the extracted chains to a BERT-based QA model to do final answer prediction.