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        <title>Adaptive Semiparametric Language Models</title>
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    <name type="personal">
        <namePart type="given">Dani</namePart>
        <namePart type="family">Yogatama</namePart>
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        <namePart type="given">Cyprien</namePart>
        <namePart type="family">de Masson d’Autume</namePart>
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    <name type="personal">
        <namePart type="given">Lingpeng</namePart>
        <namePart type="family">Kong</namePart>
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    <abstract>Abstract We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our model uses extended short-term context by caching local hidden states—similar to transformer-XL—and global long-term memory by retrieving a set of nearest neighbor tokens at each timestep. We design a gating function to adaptively combine multiple information sources to make a prediction. This mechanism allows the model to use either local context, short-term memory, or long-term memory (or any combination of them) on an ad hoc basis depending on the context. Experiments on word-based and character-based language modeling datasets demonstrate the efficacy of our proposed method compared to strong baselines.</abstract>
    <identifier type="citekey">yogatama-etal-2021-adaptive</identifier>
    <identifier type="doi">10.1162/tacl_a_00371</identifier>
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    <part>
        <date>2021</date>
        <detail type="volume"><number>9</number></detail>
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            <start>362</start>
            <end>373</end>
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%0 Journal Article
%T Adaptive Semiparametric Language Models
%A Yogatama, Dani
%A de Masson d’Autume, Cyprien
%A Kong, Lingpeng
%J Transactions of the Association for Computational Linguistics
%D 2021
%V 9
%I MIT Press
%C Cambridge, MA
%F yogatama-etal-2021-adaptive
%X Abstract We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our model uses extended short-term context by caching local hidden states—similar to transformer-XL—and global long-term memory by retrieving a set of nearest neighbor tokens at each timestep. We design a gating function to adaptively combine multiple information sources to make a prediction. This mechanism allows the model to use either local context, short-term memory, or long-term memory (or any combination of them) on an ad hoc basis depending on the context. Experiments on word-based and character-based language modeling datasets demonstrate the efficacy of our proposed method compared to strong baselines.
%9 journal article
%R 10.1162/tacl_a_00371
%U https://aclanthology.org/2021.tacl-1.22
%U https://doi.org/10.1162/tacl_a_00371
%P 362-373

Markdown (Informal)

[Adaptive Semiparametric Language Models](https://aclanthology.org/2021.tacl-1.22) (Yogatama et al., TACL 2021)

• Adaptive Semiparametric Language Models (Yogatama et al., TACL 2021)

ACL

• Dani Yogatama, Cyprien de Masson d’Autume, and Lingpeng Kong. 2021. Adaptive Semiparametric Language Models. Transactions of the Association for Computational Linguistics, 9:362–373.