RecMind：带有寻求者内部状态的日语电影推荐对话
RecMind: Japanese Movie Recommendation Dialogue with Seeker's Internal State
AI总结：
本文构建了带有实体级内部状态标注的日语电影推荐对话数据集RecMind，发现寻求者不了解但感兴趣的实体有助于推荐成功，并提出利用思维链提示显式建模内部状态的响应生成框架，在一致性和推荐成功率上优于基线。
摘要：
人类在对话中会仔细关注对话者的内部状态。例如，在推荐对话中，我们在进行推荐的同时会估计寻求者的内部状态，比如其知识水平和兴趣程度。由于目前没有用于此类分析的已标注资源，我们构建了RecMind，这是一个日语电影推荐对话数据集，在实体级别上标注了寻求者的内部状态。每个实体都有一个由寻求者标注的主观标签和一个由推荐者标注的客观标签。RecMind还包含具有较长寻求者话语的引人入胜的对话，从而能够对寻求者的内部状态进行详细分析。我们基于RecMind的分析表明，寻求者不了解但感兴趣的实体有助于推荐成功。我们还提出了一个明确考虑寻求者内部状态的响应生成框架，利用思维链提示（chain-of-thought prompting）。人工评估结果表明，我们提出的方法在一致性和推荐成功率方面均优于基线方法。
英文摘要：
Humans pay careful attention to the interlocutor's internal state in dialogues. For example, in recommendation dialogues, we make recommendations while estimating the seeker's internal state, such as his/her level of knowledge and interest. Since there are no existing annotated resources for the analysis, we constructed RecMind, a Japanese movie recommendation dialogue dataset with annotations of the seeker's internal state at the entity level. Each entity has a subjective label annotated by the seeker and an objective label annotated by the recommender. RecMind also features engaging dialogues with long seeker's utterances, enabling a detailed analysis of the seeker's internal state. Our analysis based on RecMind reveals that entities that the seeker has no knowledge about but has an interest in contribute to recommendation success. We also propose a response generation framework that explicitly considers the seeker's internal state, utilizing the chain-of-thought prompting. The human evaluation results show that our proposed method outperforms the baseline method in both consistency and the success of recommendations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
