  Few-shot relation extraction (FSRE) aims at recognizing unseen relations by
learning with merely a handful of annotated instances. To generalize to new
relations more effectively, this paper proposes a novel pipeline for the FSRE
task based on queRy-information guided Attention and adaptive Prototype fuSion,
namely RAPS. Specifically, RAPS first derives the relation prototype by the
query-information guided attention module, which exploits rich interactive
information between the support instances and the query instances, in order to
obtain more accurate initial prototype representations. Then RAPS elaborately
combines the derived initial prototype with the relation information by the
adaptive prototype fusion mechanism to get the integrated prototype for both
train and prediction. Experiments on the benchmark dataset FewRel 1.0 show a
significant improvement of our method against state-of-the-art methods.
