  Figurative language (e.g., "he flew like the wind") is challenging to
understand, as it is hard to tell what implicit information is being conveyed
from the surface form alone. We hypothesize that to perform this task well, the
reader needs to mentally elaborate the scene being described to identify a
sensible meaning of the language. We present DREAM-FLUTE, a figurative language
understanding system that does this, first forming a "mental model" of
situations described in a premise and hypothesis before making an
entailment/contradiction decision and generating an explanation. DREAM-FLUTE
uses an existing scene elaboration model, DREAM, for constructing its "mental
model." In the FigLang2022 Shared Task evaluation, DREAM-FLUTE achieved (joint)
first place (Acc@60=63.3%), and can perform even better with ensemble
techniques, demonstrating the effectiveness of this approach. More generally,
this work suggests that adding a reflective component to pretrained language
models can improve their performance beyond standard fine-tuning (3.3%
improvement in Acc@60).

Computation and Language