  When people answer questions about a specific situation, e.g., "I cheated on
my mid-term exam last week. Was that wrong?", cognitive science suggests that
they form a mental picture of that situation before answering. While we do not
know how language models (LMs) answer such questions, we conjecture that they
may answer more accurately if they are also provided with additional details
about the question situation, elaborating the "scene". To test this conjecture,
we train a new model, DREAM, to answer questions that elaborate the scenes that
situated questions are about, and then provide those elaborations as additional
context to a question-answering (QA) model. We find that DREAM is able to
create better scene elaborations (more accurate, useful, and consistent) than a
representative state-of-the-art, zero-shot model (Macaw). We also find that
using the scene elaborations as additional context improves the answer accuracy
of a downstream QA system, including beyond that obtainable by simply further
finetuning the QA system on DREAM's training data. These results suggest that
adding focused elaborations about a situation can improve a system's reasoning
about it, and may serve as an effective way of injecting new scenario based
knowledge into QA models. Finally, our approach is dataset-neutral; we observe
improved QA performance across different models, with even bigger gains on
models with fewer parameters. We make our dataset and model publicly available
at https://github.com/allenai/dream.
