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.

翻译：对话式信息搜寻（CIS）是一种新兴的知识获取与探索性搜索范式。传统网络搜索界面虽能便捷探索实体，但在对话场景中受限于窄带宽接口。本文探索CIS中的答案重写方法，使用户无需借助外部服务或来源即可理解答案。具体而言，我们聚焦于显著实体——即理解答案的核心实体。作为首个贡献，我们构建了标注实体显著性的对话数据集。数据分析表明，多数答案包含显著实体。作为第二个贡献，我们提出两种面向提升CIS用户体验的答案重写策略：其一通过添加显著实体的内联定义扩充答案，使其具备自包含性；其二通过补充追问问题完善答案，使用户能够深入了解特定实体。基于众包研究的结果显示，重写后的答案明显优于原始答案。我们还发现内联定义更受青睐，但这一选择具有高度主观性，这为个性化研究开辟了具有前景的方向。