Conversational interactions have reshaped information retrieval systems, as users increasingly favour direct answers over traditional hyperlinks. To build reliable Conversational Information Access (CIA) systems that account for personal context, this thesis addresses challenges: (1) personal context extraction, (2) personalized response generation, and (3) effective and interpretable system evaluation. First, we tackle personal context extraction by studying what Entity Linking (EL) in conversations entails, introducing a dataset for conversational entity linking (ConEL), and proposing CREL, a novel EL method tailored for conversational settings. Second, we focus on personalized response generation by proposing LAPS, a method for efficiently constructing large-scale, human-written, personalized conversational datasets, and using them to study how users' preferences can be utilized to generate personalized responses. Finally, we address the need for effective and interpretable system evaluation by introducing FACE, an automatic, reference-free method that assesses entire conversations and aligns closely with human judgments.

翻译：对话式交互已从根本上改变了信息检索系统，用户日益倾向于直接获取答案而非传统超链接。为构建能融合个人情境的可靠对话式信息获取系统，本论文聚焦三大挑战：（1）个人情境提取；（2）个性化回复生成；（3）有效且可解读的系统评估。首先，针对个人情境提取，我们系统研究了对话场景中的实体链接问题，构建了对话实体链接（ConEL）数据集，并提出了专为对话场景设计的创新性实体链接方法CREL。其次，在个性化回复生成方面，我们提出LAPS方法，该方法能高效构建大规模人工撰写的个性化对话数据集，并利用这些数据探究如何通过用户偏好生成个性化回复。最后，为满足系统评估的有效性与可解读性需求，我们提出FACE——一种自动化的无参考评估方法，可对完整对话进行评测，其评估结果与人工判断高度一致。