  Judging the veracity of a sentence making one or more claims is an important
and challenging problem with many dimensions. The recent FEVER task asked
participants to classify input sentences as either SUPPORTED, REFUTED or
NotEnoughInfo using Wikipedia as a source of true facts. SURFACE does this task
and explains its decision through a selection of sentences from the trusted
source. Our multi-task neural approach uses semantic lexical frames from
FrameNet to jointly (i) find relevant evidential sentences in the trusted
source and (ii) use them to classify the input sentence's veracity. An
evaluation of our efficient three-parameter model on the FEVER dataset showed
an improvement of 90% over the state-of-the-art baseline on retrieving relevant
sentences and a 70% relative improvement in classification.
