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        <title>Collective Human Opinions in Semantic Textual Similarity</title>
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        <namePart type="given">Yuxia</namePart>
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    <name type="personal">
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    <abstract>Despite the subjective nature of semantic textual similarity (STS) and pervasive disagreements in STS annotation, existing benchmarks have used averaged human ratings as gold standard. Averaging masks the true distribution of human opinions on examples of low agreement, and prevents models from capturing the semantic vagueness that the individual ratings represent. In this work, we introduce USTS, the first Uncertainty-aware STS dataset with ∼15,000 Chinese sentence pairs and 150,000 labels, to study collective human opinions in STS. Analysis reveals that neither a scalar nor a single Gaussian fits a set of observed judgments adequately. We further show that current STS models cannot capture the variance caused by human disagreement on individual instances, but rather reflect the predictive confidence over the aggregate dataset.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00584</identifier>
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        <date>2023</date>
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            <start>997</start>
            <end>1013</end>
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%0 Journal Article
%T Collective Human Opinions in Semantic Textual Similarity
%A Wang, Yuxia
%A Tao, Shimin
%A Xie, Ning
%A Yang, Hao
%A Baldwin, Timothy
%A Verspoor, Karin
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F wang-etal-2023-collective
%X Despite the subjective nature of semantic textual similarity (STS) and pervasive disagreements in STS annotation, existing benchmarks have used averaged human ratings as gold standard. Averaging masks the true distribution of human opinions on examples of low agreement, and prevents models from capturing the semantic vagueness that the individual ratings represent. In this work, we introduce USTS, the first Uncertainty-aware STS dataset with ∼15,000 Chinese sentence pairs and 150,000 labels, to study collective human opinions in STS. Analysis reveals that neither a scalar nor a single Gaussian fits a set of observed judgments adequately. We further show that current STS models cannot capture the variance caused by human disagreement on individual instances, but rather reflect the predictive confidence over the aggregate dataset.
%R 10.1162/tacl_a_00584
%U https://aclanthology.org/2023.tacl-1.56
%U https://doi.org/10.1162/tacl_a_00584
%P 997-1013

Markdown (Informal)

[Collective Human Opinions in Semantic Textual Similarity](https://aclanthology.org/2023.tacl-1.56) (Wang et al., TACL 2023)

• Collective Human Opinions in Semantic Textual Similarity (Wang et al., TACL 2023)

ACL

• Yuxia Wang, Shimin Tao, Ning Xie, Hao Yang, Timothy Baldwin, and Karin Verspoor. 2023. Collective Human Opinions in Semantic Textual Similarity. Transactions of the Association for Computational Linguistics, 11:997–1013.