  A common thread of open-domain question answering (QA) models employs a
retriever-reader pipeline that first retrieves a handful of relevant passages
from Wikipedia and then peruses the passages to produce an answer. However,
even state-of-the-art readers fail to capture the complex relationships between
entities appearing in questions and retrieved passages, leading to answers that
contradict the facts. In light of this, we propose a novel knowledge Graph
enhanced passage reader, namely Grape, to improve the reader performance for
open-domain QA. Specifically, for each pair of question and retrieved passage,
we first construct a localized bipartite graph, attributed to entity embeddings
extracted from the intermediate layer of the reader model. Then, a graph neural
network learns relational knowledge while fusing graph and contextual
representations into the hidden states of the reader model. Experiments on
three open-domain QA benchmarks show Grape can improve the state-of-the-art
performance by up to 2.2 exact match score with a negligible overhead increase,
with the same retriever and retrieved passages. Our code is publicly available
at https://github.com/jumxglhf/GRAPE.
