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        <title>Human Sentence Processing: Recurrence or Attention?</title>
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        <namePart type="given">Danny</namePart>
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            <title>Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics</title>
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            <namePart type="given">Emmanuele</namePart>
            <namePart type="family">Chersoni</namePart>
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        <name type="personal">
            <namePart type="given">Nora</namePart>
            <namePart type="family">Hollenstein</namePart>
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        <name type="personal">
            <namePart type="given">Cassandra</namePart>
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            <namePart type="given">Yohei</namePart>
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            <namePart type="given">Laurent</namePart>
            <namePart type="family">Prévot</namePart>
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        <name type="personal">
            <namePart type="given">Enrico</namePart>
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    <abstract>Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks but little is known about its ability to model human language processing. We compare Transformer- and RNN-based language models’ ability to account for measures of human reading effort. Our analysis shows Transformers to outperform RNNs in explaining self-paced reading times and neural activity during reading English sentences, challenging the widely held idea that human sentence processing involves recurrent and immediate processing and provides evidence for cue-based retrieval.</abstract>
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%0 Conference Proceedings
%T Human Sentence Processing: Recurrence or Attention?
%A Merkx, Danny
%A Frank, Stefan L.
%Y Chersoni, Emmanuele
%Y Hollenstein, Nora
%Y Jacobs, Cassandra
%Y Oseki, Yohei
%Y Prévot, Laurent
%Y Santus, Enrico
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F merkx-frank-2021-human
%X Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks but little is known about its ability to model human language processing. We compare Transformer- and RNN-based language models’ ability to account for measures of human reading effort. Our analysis shows Transformers to outperform RNNs in explaining self-paced reading times and neural activity during reading English sentences, challenging the widely held idea that human sentence processing involves recurrent and immediate processing and provides evidence for cue-based retrieval.
%R 10.18653/v1/2021.cmcl-1.2
%U https://aclanthology.org/2021.cmcl-1.2
%U https://doi.org/10.18653/v1/2021.cmcl-1.2
%P 12-22

Markdown (Informal)

[Human Sentence Processing: Recurrence or Attention?](https://aclanthology.org/2021.cmcl-1.2) (Merkx & Frank, CMCL 2021)

• Human Sentence Processing: Recurrence or Attention? (Merkx & Frank, CMCL 2021)

ACL

• Danny Merkx and Stefan L. Frank. 2021. Human Sentence Processing: Recurrence or Attention?. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pages 12–22, Online. Association for Computational Linguistics.