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.

本文提出了 Dense Paraphrasing（DP）这一文本表征转化的过程，即通过对文本表达（词汇或词组）进行重写以减少歧义并使底层语义得到明确的阐述，从而改善推理和问答任务性能。同时，作者介绍了第一个完整的 DP 数据集及注释任务的范畴和设计，并通过实验取得了令人满意的结果。