论文标题
安排或不安排：提取特定任务的时间实体和相关的否定约束
To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints
论文作者
论文摘要
从文本中提取日期时间实体的最新研究是任务不可知。因此，尽管文献中提出的方法在文本中提取的通用日期时间提取效果很好，但它们在特定任务日期时间实体提取方面的表现不佳，而文本中仅存在的日期时间实体的子集与解决任务有关。此外，某些任务需要识别与日期时间实体相关的否定约束，以正确地推理。我们展示了一种新颖的模型，用于提取特定任务的日期实体及其负约束。我们在调度会议的上下文中为基于电子邮件的数字AI调度助手的调度会议的上下文中展示了我们方法对日期时间理解的任务的功效。与检测与调度会议相关的日期时间实体相比，我们的方法获得了19 \％F评分点的绝对增益，比基线方法提高了4 \％的改进，以检测到与日期时间实体相比的否定约束。
State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in literature perform well for generic date-time extraction from texts, they don't fare as well on task specific date-time entity extraction where only a subset of the date-time entities present in the text are pertinent to solving the task. Furthermore, some tasks require identifying negation constraints associated with the date-time entities to correctly reason over time. We showcase a novel model for extracting task-specific date-time entities along with their negation constraints. We show the efficacy of our method on the task of date-time understanding in the context of scheduling meetings for an email-based digital AI scheduling assistant. Our method achieves an absolute gain of 19\% f-score points compared to baseline methods in detecting the date-time entities relevant to scheduling meetings and a 4\% improvement over baseline methods for detecting negation constraints over date-time entities.
