Every word counts: A multilingual analysis of individual human alignment with model attention

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Every word counts : A multilingual analysis of individual human alignment with model attention. / Brandl, Stephanie; Hollenstein, Nora.

Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Stroudsburg, PA : Association for Computational Linguistics, 2022. p. 72-77.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Brandl, S & Hollenstein, N 2022, Every word counts: A multilingual analysis of individual human alignment with model attention. in Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, Stroudsburg, PA, pp. 72-77. <https://aclanthology.org/2022.aacl-short.10>

APA

Brandl, S., & Hollenstein, N. (2022). Every word counts: A multilingual analysis of individual human alignment with model attention. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (pp. 72-77). Association for Computational Linguistics. https://aclanthology.org/2022.aacl-short.10

Vancouver

Brandl S, Hollenstein N. Every word counts: A multilingual analysis of individual human alignment with model attention. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Stroudsburg, PA: Association for Computational Linguistics. 2022. p. 72-77

Author

Brandl, Stephanie ; Hollenstein, Nora. / Every word counts : A multilingual analysis of individual human alignment with model attention. Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Stroudsburg, PA : Association for Computational Linguistics, 2022. pp. 72-77

Bibtex

@inproceedings{bcffd19b44694a54b950c0204a9a01b6,
title = "Every word counts: A multilingual analysis of individual human alignment with model attention",
abstract = "Human fixation patterns have been shown to correlate strongly with Transformer-based attention. Those correlation analyses are usually carried out without taking into account individual differences between participants and are mostly done on monolingual datasets making it difficult to generalise findings. In this paper, we analyse eye-tracking data from speakers of 13 different languages reading both in their native language (L1) and in English as language learners (L2). We find considerable differences between languages but also that individual reading behaviour such as skipping rate, total reading time and vocabulary knowledge (LexTALE) influence the alignment between humans and models to an extent that should be considered in future studies.",
author = "Stephanie Brandl and Nora Hollenstein",
year = "2022",
language = "English",
pages = "72--77",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
publisher = "Association for Computational Linguistics",

}

RIS

TY - GEN

T1 - Every word counts

T2 - A multilingual analysis of individual human alignment with model attention

AU - Brandl, Stephanie

AU - Hollenstein, Nora

PY - 2022

Y1 - 2022

N2 - Human fixation patterns have been shown to correlate strongly with Transformer-based attention. Those correlation analyses are usually carried out without taking into account individual differences between participants and are mostly done on monolingual datasets making it difficult to generalise findings. In this paper, we analyse eye-tracking data from speakers of 13 different languages reading both in their native language (L1) and in English as language learners (L2). We find considerable differences between languages but also that individual reading behaviour such as skipping rate, total reading time and vocabulary knowledge (LexTALE) influence the alignment between humans and models to an extent that should be considered in future studies.

AB - Human fixation patterns have been shown to correlate strongly with Transformer-based attention. Those correlation analyses are usually carried out without taking into account individual differences between participants and are mostly done on monolingual datasets making it difficult to generalise findings. In this paper, we analyse eye-tracking data from speakers of 13 different languages reading both in their native language (L1) and in English as language learners (L2). We find considerable differences between languages but also that individual reading behaviour such as skipping rate, total reading time and vocabulary knowledge (LexTALE) influence the alignment between humans and models to an extent that should be considered in future studies.

M3 - Article in proceedings

SP - 72

EP - 77

BT - Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

PB - Association for Computational Linguistics

CY - Stroudsburg, PA

ER -

ID: 331507311