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        <title>Relational Memory-Augmented Language Models</title>
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    <name type="personal">
        <namePart type="given">Qi</namePart>
        <namePart type="family">Liu</namePart>
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        <namePart type="given">Dani</namePart>
        <namePart type="family">Yogatama</namePart>
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    <abstract>We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve relevant relations for a given context to improve text generation. Experiments on WikiText-103, WMT19, and enwik8 English datasets demonstrate that our approach produces a better language model in terms of perplexity and bits per character. We also show that relational memory improves coherence, is complementary to token-based memory, and enables causal interventions. Our model provides a simple yet effective way to combine an autoregressive language model and a knowledge graph for more coherent and logical generation.</abstract>
    <identifier type="citekey">liu-etal-2022-relational</identifier>
    <identifier type="doi">10.1162/tacl_a_00476</identifier>
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        <date>2022</date>
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%0 Journal Article
%T Relational Memory-Augmented Language Models
%A Liu, Qi
%A Yogatama, Dani
%A Blunsom, Phil
%J Transactions of the Association for Computational Linguistics
%D 2022
%V 10
%I MIT Press
%C Cambridge, MA
%F liu-etal-2022-relational
%X We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve relevant relations for a given context to improve text generation. Experiments on WikiText-103, WMT19, and enwik8 English datasets demonstrate that our approach produces a better language model in terms of perplexity and bits per character. We also show that relational memory improves coherence, is complementary to token-based memory, and enables causal interventions. Our model provides a simple yet effective way to combine an autoregressive language model and a knowledge graph for more coherent and logical generation.
%R 10.1162/tacl_a_00476
%U https://aclanthology.org/2022.tacl-1.32
%U https://doi.org/10.1162/tacl_a_00476
%P 555-572

Markdown (Informal)

[Relational Memory-Augmented Language Models](https://aclanthology.org/2022.tacl-1.32) (Liu et al., TACL 2022)

• Relational Memory-Augmented Language Models (Liu et al., TACL 2022)

ACL

• Qi Liu, Dani Yogatama, and Phil Blunsom. 2022. Relational Memory-Augmented Language Models. Transactions of the Association for Computational Linguistics, 10:555–572.