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        <title>Cross-Lingual Transfer of Cognitive Processing Complexity</title>
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        <namePart type="given">Charlotte</namePart>
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    <name type="personal">
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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            <namePart type="given">Isabelle</namePart>
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    <abstract>When humans read a text, their eye movements are influenced by the structural complexity of the input sentences. This cognitive phenomenon holds across languages and recent studies indicate that multilingual language models utilize structural similarities between languages to facilitate cross-lingual transfer. We use sentence-level eye-tracking patterns as a cognitive indicator for structural complexity and show that the multilingual model XLM-RoBERTa can successfully predict varied patterns for 13 typologically diverse languages, despite being fine-tuned only on English data. We quantify the sensitivity of the model to structural complexity and distinguish a range of complexity characteristics. Our results indicate that the model develops a meaningful bias towards sentence length but also integrates cross-lingual differences. We conduct a control experiment with randomized word order and find that the model seems to additionally capture more complex structural information.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.49</identifier>
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%0 Conference Proceedings
%T Cross-Lingual Transfer of Cognitive Processing Complexity
%A Pouw, Charlotte
%A Hollenstein, Nora
%A Beinborn, Lisa
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F pouw-etal-2023-cross
%X When humans read a text, their eye movements are influenced by the structural complexity of the input sentences. This cognitive phenomenon holds across languages and recent studies indicate that multilingual language models utilize structural similarities between languages to facilitate cross-lingual transfer. We use sentence-level eye-tracking patterns as a cognitive indicator for structural complexity and show that the multilingual model XLM-RoBERTa can successfully predict varied patterns for 13 typologically diverse languages, despite being fine-tuned only on English data. We quantify the sensitivity of the model to structural complexity and distinguish a range of complexity characteristics. Our results indicate that the model develops a meaningful bias towards sentence length but also integrates cross-lingual differences. We conduct a control experiment with randomized word order and find that the model seems to additionally capture more complex structural information.
%R 10.18653/v1/2023.findings-eacl.49
%U https://aclanthology.org/2023.findings-eacl.49
%U https://doi.org/10.18653/v1/2023.findings-eacl.49
%P 655-669

Markdown (Informal)

[Cross-Lingual Transfer of Cognitive Processing Complexity](https://aclanthology.org/2023.findings-eacl.49) (Pouw et al., Findings 2023)

• Cross-Lingual Transfer of Cognitive Processing Complexity (Pouw et al., Findings 2023)

ACL

• Charlotte Pouw, Nora Hollenstein, and Lisa Beinborn. 2023. Cross-Lingual Transfer of Cognitive Processing Complexity. In Findings of the Association for Computational Linguistics: EACL 2023, pages 655–669, Dubrovnik, Croatia. Association for Computational Linguistics.