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        <title>Diachronic degradation of language models: Insights from social media</title>
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        <namePart type="given">Kokil</namePart>
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        <namePart type="given">Niyati</namePart>
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    <name type="personal">
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>Natural languages change over time because they evolve to the needs of their users and the socio-technological environment. This study investigates the diachronic accuracy of pre-trained language models for downstream tasks in machine learning and user profiling. It asks the question: given that the social media platform and its users remain the same, how is language changing over time? How can these differences be used to track the changes in the affect around a particular topic? To our knowledge, this is the first study to show that it is possible to measure diachronic semantic drifts within social media and within the span of a few years.</abstract>
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    <identifier type="doi">10.18653/v1/P18-2032</identifier>
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%0 Conference Proceedings
%T Diachronic degradation of language models: Insights from social media
%A Jaidka, Kokil
%A Chhaya, Niyati
%A Ungar, Lyle
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F jaidka-etal-2018-diachronic
%X Natural languages change over time because they evolve to the needs of their users and the socio-technological environment. This study investigates the diachronic accuracy of pre-trained language models for downstream tasks in machine learning and user profiling. It asks the question: given that the social media platform and its users remain the same, how is language changing over time? How can these differences be used to track the changes in the affect around a particular topic? To our knowledge, this is the first study to show that it is possible to measure diachronic semantic drifts within social media and within the span of a few years.
%R 10.18653/v1/P18-2032
%U https://aclanthology.org/P18-2032
%U https://doi.org/10.18653/v1/P18-2032
%P 195-200

Markdown (Informal)

[Diachronic degradation of language models: Insights from social media](https://aclanthology.org/P18-2032) (Jaidka et al., ACL 2018)

• Diachronic degradation of language models: Insights from social media (Jaidka et al., ACL 2018)

ACL

• Kokil Jaidka, Niyati Chhaya, and Lyle Ungar. 2018. Diachronic degradation of language models: Insights from social media. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 195–200, Melbourne, Australia. Association for Computational Linguistics.