A user input to a schema-driven dialogue information navigation system, such as venue search, is typically constrained by the underlying database which restricts the user to specify a predefined set of preferences, or slots, corresponding to the database fields. We envision a more natural information navigation dialogue interface where a user has flexibility to specify unconstrained preferences that may not match a predefined schema. We propose to use information retrieval from unstructured knowledge to identify entities relevant to a user request. We update the Cambridge restaurants database with unstructured knowledge snippets (reviews and information from the web) for each of the restaurants and annotate a set of query-snippet pairs with a relevance label. We use the annotated dataset to train and evaluate snippet relevance classifiers, as a proxy to evaluating recommendation accuracy. We show that with a pretrained transformer model as an encoder, an unsupervised/supervised classifier achieves a weighted F1 of .661/.856.

中文翻译：

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在对话中处理不受约束的用户偏好

对模式驱动的对话信息导航系统（例如场所搜索）的用户输入通常受基础数据库的约束，该数据库限制用户指定与数据库字段相对应的一组预定义的偏好或槽位。我们设想了一个更自然的信息导航对话界面，其中用户可以灵活地指定可能与预定义模式不匹配的不受约束的偏好。我们建议使用来自非结构化知识的信息检索来识别与用户请求相关的实体。我们使用每个餐厅的非结构化知识片段（来自网络的评论和信息）更新剑桥餐厅数据库，并使用相关标签注释一组查询片段对。我们使用带注释的数据集来训练和评估片段相关性分类器，作为评估推荐准确性的代理。我们表明，使用预训练的 Transformer 模型作为编码器，无监督/监督分类器实现了 0.661/.856 的加权 F1。