  Qualitative relationships describe how increasing or decreasing one property
(e.g. altitude) affects another (e.g. temperature). They are an important
aspect of natural language question answering and are crucial for building
chatbots or voice agents where one may enquire about qualitative relationships.
Recently a dataset about question answering involving qualitative relationships
has been proposed, and a few approaches to answer such questions have been
explored, in the heart of which lies a semantic parser that converts the
natural language input to a suitable logical form. A problem with existing
semantic parsers is that they try to directly convert the input sentences to a
logical form. Since the output language varies with each application, it forces
the semantic parser to learn almost everything from scratch. In this paper, we
show that instead of using a semantic parser to produce the logical form, if we
apply the generate-validate framework i.e. generate a natural language
description of the logical form and validate if the natural language
description is followed from the input text, we get a better scope for transfer
learning and our method outperforms the state-of-the-art by a large margin of
7.93%.
