Multi-hop Reading Comprehension across Multiple Documents by
Reasoning over Heterogeneous Graphs
Abstract
Multi-hop reading comprehension (RC) across
documents poses new challenge over singledocument RC because it requires reasoning
over multiple documents to reach the final
answer. In this paper, we propose a new
model to tackle the multi-hop RC problem.
We introduce a heterogeneous graph with
different types of nodes and edges, which
is named as Heterogeneous Document-Entity
(HDE) graph. The advantage of HDE graph
is that it contains different granularity levels of information including candidates, documents and entities in specific document contexts. Our proposed model can do reasoning
over the HDE graph with nodes representation
initialized with co-attention and self-attention
based context encoders. We employ Graph
Neural Networks (GNN) based message passing algorithms to accumulate evidences on the
proposed HDE graph. Evaluated on the blind
test set of the Qangaroo WIKIHOP data set,
our HDE graph based single model delivers
competitive result, and the ensemble model
achieves the state-of-the-art performance