论文标题
Inspired2：改进的数据集用于社交对话建议
INSPIRED2: An Improved Dataset for Sociable Conversational Recommendation
论文作者
论文摘要
能够以自然语言与用户互动的会话推荐系统（CRS）通常会使用以前在配对人类的帮助下收集的建议对话框，其中一个人扮演寻求者的角色，而另一个则是推荐人。这些建议对话框包括指示用户喜好的项目和实体。为了精确地对寻求者的偏好进行建模并始终如一地做出反应，CRS通常依赖于项目和实体注释。启发了此类数据集的最新示例，其中包括有关社交对话建议的建议对话框，其中使用自动关键字或图案匹配技术对项目和实体进行注释。不幸的是，对该数据集的分析表明，有大量案例错误地注释了项目和实体，或者完全缺少注释。这导致了一个问题在多大程度上有效的自动技术有效。此外，重要的是研究注释质量对系统响应质量的CRS总体有效性的影响。为了研究这些方面，我们在受启发中手动修复了注释。然后，我们使用两个版本的数据集评估了多个基准CR的性能。我们的分析表明，数据集的改进版本，即Inspired2，有助于提高多个基准CR的性能，强调数据质量对于端到端学习和基于基于基于检索的会话建议的方法的重要性。我们在https://github.com/ahtsham58/inspired2公开发布改进的数据集（Inspired2）。
Conversational recommender systems (CRS) that are able to interact with users in natural language often utilize recommendation dialogs which were previously collected with the help of paired humans, where one plays the role of a seeker and the other as a recommender. These recommendation dialogs include items and entities that indicate the users' preferences. In order to precisely model the seekers' preferences and respond consistently, CRS typically rely on item and entity annotations. A recent example of such a dataset is INSPIRED, which consists of recommendation dialogs for sociable conversational recommendation, where items and entities were annotated using automatic keyword or pattern matching techniques. An analysis of this dataset unfortunately revealed that there is a substantial number of cases where items and entities were either wrongly annotated or annotations were missing at all. This leads to the question to what extent automatic techniques for annotations are effective. Moreover, it is important to study impact of annotation quality on the overall effectiveness of a CRS in terms of the quality of the system's responses. To study these aspects, we manually fixed the annotations in INSPIRED. We then evaluated the performance of several benchmark CRS using both versions of the dataset. Our analyses suggest that the improved version of the dataset, i.e., INSPIRED2, helped increase the performance of several benchmark CRS, emphasizing the importance of data quality both for end-to-end learning and retrieval-based approaches to conversational recommendation. We release our improved dataset (INSPIRED2) publicly at https://github.com/ahtsham58/INSPIRED2.
