  Many natural language questions require recognizing and reasoning with
qualitative relationships (e.g., in science, economics, and medicine), but are
challenging to answer with corpus-based methods. Qualitative modeling provides
tools that support such reasoning, but the semantic parsing task of mapping
questions into those models has formidable challenges. We present QuaRel, a
dataset of diverse story questions involving qualitative relationships that
characterize these challenges, and techniques that begin to address them. The
dataset has 2771 questions relating 19 different types of quantities. For
example, "Jenny observes that the robot vacuum cleaner moves slower on the
living room carpet than on the bedroom carpet. Which carpet has more friction?"
We contribute (1) a simple and flexible conceptual framework for representing
these kinds of questions; (2) the QuaRel dataset, including logical forms,
exemplifying the parsing challenges; and (3) two novel models for this task,
built as extensions of type-constrained semantic parsing. The first of these
models (called QuaSP+) significantly outperforms off-the-shelf tools on QuaRel.
The second (QuaSP+Zero) demonstrates zero-shot capability, i.e., the ability to
handle new qualitative relationships without requiring additional training
data, something not possible with previous models. This work thus makes inroads
into answering complex, qualitative questions that require reasoning, and
scaling to new relationships at low cost. The dataset and models are available
at http://data.allenai.org/quarel.
