Towards Self-Contained Answers: Entity-Based Answer Rewriting in
  Conversational Search
Ivan Sekulić Krisztian Balog Fabio Crestani
Conversational information-seeking (CIS) is an emerging paradigm for
knowledge acquisition and exploratory search. Traditional web search interfaces
enable easy exploration of entities, but this is limited in conversational
settings due to the limited-bandwidth interface. This paper explore ways to
rewrite answers in CIS, so that users can understand them without having to
resort to external services or sources. Specifically, we focus on salient
entities -- entities that are central to understanding the answer. As our first
contribution, we create a dataset of conversations annotated with entities for
saliency. Our analysis of the collected data reveals that the majority of
answers contain salient entities. As our second contribution, we propose two
answer rewriting strategies aimed at improving the overall user experience in
CIS. One approach expands answers with inline definitions of salient entities,
making the answer self-contained. The other approach complements answers with
follow-up questions, offering users the possibility to learn more about
specific entities. Results of a crowdsourcing-based study indicate that
rewritten answers are clearly preferred over the original ones. We also find
that inline definitions tend to be favored over follow-up questions, but this
choice is highly subjective, thereby providing a promising future direction for
personalization.