  Understanding rich narratives, such as dialogues and stories, often requires
natural language processing systems to access relevant knowledge from
commonsense knowledge graphs. However, these systems typically retrieve facts
from KGs using simple heuristics that disregard the complex challenges of
identifying situationally-relevant commonsense knowledge (e.g.,
contextualization, implicitness, ambiguity).
  In this work, we propose the new task of commonsense fact linking, where
models are given contexts and trained to identify situationally-relevant
commonsense knowledge from KGs. Our novel benchmark, ComFact, contains ~293k
in-context relevance annotations for commonsense triplets across four
stylistically diverse dialogue and storytelling datasets. Experimental results
confirm that heuristic fact linking approaches are imprecise knowledge
extractors. Learned fact linking models demonstrate across-the-board
performance improvements (~34.6% F1) over these heuristics. Furthermore,
improved knowledge retrieval yielded average downstream improvements of 9.8%
for a dialogue response generation task. However, fact linking models still
significantly underperform humans, suggesting our benchmark is a promising
testbed for research in commonsense augmentation of NLP systems.
