让对话式信息检索更懂用户，从理解上下文到生成个性化回答。
Personalization and Evaluation of Conversational Information Access
- 提出ConEL数据集与CREL方法，精准提取对话中的实体上下文。
- 构建LAPS数据集，用真人对话训练模型生成符合用户偏好的回答。
- 设计FACE评估法，无需参考答案即可准确衡量对话质量。
对话式交互正重塑信息检索系统，用户更倾向直接获取答案而非传统超链接。为构建能考虑个人背景的可靠对话式信息访问（CIA）系统，本文解决三大挑战：（1）个人上下文提取，（2）个性化回复生成，（3）有效且可解释的系统评估。首先，通过研究对话中的实体链接（EL）特性，引入对话实体链接数据集（ConEL），并提出专为对话场景设计的CREL方法。其次，提出LAPS方法，高效构建大规模、人工撰写、个性化的对话数据集，并利用其研究如何利用用户偏好生成个性化回复。最后，提出FACE方法，一种自动、无需参考文本的评估方式，能全面评估对话表现，与人类判断高度一致。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。