Detecting Temporal Ambiguity in Questions
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 to study the task of detecting the temporally ambiguous questions. We propose a novel approach for detecting temporally ambiguous questions 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.
Paper Type: Short
Research Area: Question Answering
Research Area Keywords: open-domain QA, corpus creation
Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources
Languages Studied: English
Submission Number: 1844
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