To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints

TL;DR

This paper introduces a novel model for extracting task-specific date-time entities and their negation constraints from text, significantly improving accuracy in scheduling-related applications.

Contribution

The paper presents a new approach tailored for task-specific date-time extraction and negation detection, outperforming existing generic methods.

Findings

19% absolute gain in detecting relevant date-time entities

4% improvement in negation constraint detection

Effective application in email-based scheduling assistants

Abstract

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.