Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension

The current trend of extractive question answering (QA) heavily relies on the joint encoding of the document and the question. In this paper, we formalize a new modular variant of extractive QA, Phrase-Indexed Question Answering (PI-QA), that enforces complete independence of the document encoder from the question by building the standalone representation of the document discourse, a key research goal in machine reading comprehension. That is, the document encoder generates an index vector for each answer candidate phrase in the document; at inference time, each question is mapped to the same vector space and the answer with the nearest index vector is obtained. The formulation also implies a significant scalability advantage since the index vectors can be pre-computed and hashed offline for efficient retrieval. We experiment with baseline models for the new task, which achieve a reasonable accuracy but significantly underperform unconstrained QA models. We invite the QA research community to engage in PI-QA for closing the gap.