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        <title>What‘s in a Name? Answer Equivalence For Open-Domain Question Answering</title>
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        <namePart type="given">Chenglei</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect. This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent answers). We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers. We analyse three QA benchmarks: Natural Questions, TriviaQA, and SQuAD. Answer expansion increases the exact match score on all datasets for evaluation, while incorporating it helps model training over real-world datasets. We ensure the additional answers are valid through a human post hoc evaluation.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.757</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T What‘s in a Name? Answer Equivalence For Open-Domain Question Answering
%A Si, Chenglei
%A Zhao, Chen
%A Boyd-Graber, Jordan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F si-etal-2021-whats
%X A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect. This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent answers). We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers. We analyse three QA benchmarks: Natural Questions, TriviaQA, and SQuAD. Answer expansion increases the exact match score on all datasets for evaluation, while incorporating it helps model training over real-world datasets. We ensure the additional answers are valid through a human post hoc evaluation.
%R 10.18653/v1/2021.emnlp-main.757
%U https://aclanthology.org/2021.emnlp-main.757/
%U https://doi.org/10.18653/v1/2021.emnlp-main.757
%P 9623-9629

Markdown (Informal)

[What’s in a Name? Answer Equivalence For Open-Domain Question Answering](https://aclanthology.org/2021.emnlp-main.757/) (Si et al., EMNLP 2021)

• What’s in a Name? Answer Equivalence For Open-Domain Question Answering (Si et al., EMNLP 2021)

ACL

• Chenglei Si, Chen Zhao, and Jordan Boyd-Graber. 2021. What’s in a Name? Answer Equivalence For Open-Domain Question Answering. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9623–9629, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.