Explore, Propose, and Assemble:
An Interpretable Model for Multi-Hop Reading Comprehension
Abstract
Multi-hop reading comprehension requires the
model to explore and connect relevant information from multiple sentences/documents in
order to answer the question about the context. To achieve this, we propose an interpretable 3-module system called ExplorePropose-Assemble reader (EPAr). First, the
Document Explorer iteratively selects relevant
documents and represents divergent reasoning
chains in a tree structure so as to allow assimilating information from all chains. The Answer Proposer then proposes an answer from
every root-to-leaf path in the reasoning tree.
Finally, the Evidence Assembler extracts a
key sentence containing the proposed answer
from every path and combines them to predict the final answer. Intuitively, EPAr approximates the coarse-to-fine-grained comprehension behavior of human readers when facing multiple long documents. We jointly optimize our 3 modules by minimizing the sum
of losses from each stage conditioned on the
previous stage’s output. On two multi-hop
reading comprehension datasets WikiHop and
MedHop, our EPAr model achieves significant
improvements over the baseline and competitive results compared to the state-of-the-art
model. We also present multiple reasoningchain-recovery tests and ablation studies to
demonstrate our system’s ability to perform interpretable and accurate reasoning.