Detecting Temporal Ambiguity in Questions

TL;DR

This paper introduces TEMPAMBIQA, a new dataset for detecting temporally ambiguous questions, and proposes novel search-based and baseline methods to identify such ambiguity in open-domain QA.

Contribution

It presents the first manually annotated dataset for temporal ambiguity detection and explores diverse search strategies and baseline models for the task.

Findings

TEMPAMBIQA contains 8,162 questions focused on temporal ambiguity.

Search strategies based on disambiguated questions improve detection accuracy.

Zero-shot and few-shot baselines provide competitive performance.

Abstract

Detecting and answering ambiguous questions has been a challenging task in open-domain question answering. Ambiguous questions have different answers depending on their interpretation and can take diverse forms. Temporally ambiguous questions are one of the most common types of such questions. In this paper, we introduce TEMPAMBIQA, a manually annotated temporally ambiguous QA dataset consisting of 8,162 open-domain questions derived from existing datasets. Our annotations focus on capturing temporal ambiguity to study the task of detecting temporally ambiguous questions. We propose a novel approach by using diverse search strategies based on disambiguated versions of the questions. We also introduce and test non-search, competitive baselines for detecting temporal ambiguity using zero-shot and few-shot approaches.