  Understanding inferences and answering questions from text requires more than
merely recovering surface arguments, adjuncts, or strings associated with the
query terms. As humans, we interpret sentences as contextualized components of
a narrative or discourse, by both filling in missing information, and reasoning
about event consequences. In this paper, we define the process of rewriting a
textual expression (lexeme or phrase) such that it reduces ambiguity while also
making explicit the underlying semantics that is not (necessarily) expressed in
the economy of sentence structure as Dense Paraphrasing (DP). We build the
first complete DP dataset, provide the scope and design of the annotation task,
and present results demonstrating how this DP process can enrich a source text
to improve inferencing and QA task performance. The data and the source code
will be publicly available.
