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            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Subword tokenization has become the de-facto standard for tokenization although comparative evaluations of their quality across languages are scarce. Existing evaluation studies focus on the effect of a tokenization algorithm on the performance in downstream tasks, or on engineering criteria such as the compression rate. We present a new evaluation paradigm that focuses on the cognitive plausibility of subword tokenization. We analyze the correlation of the tokenizer output with the reading time and accuracy of human responses on a lexical decision task. We compare three tokenization algorithms across several languages and vocabulary sizes. Our results indicate that the Unigram algorithm yields less cognitively plausible tokenization behavior and a worse coverage of derivational morphemes, in contrast with prior work.</abstract>
    <identifier type="citekey">beinborn-pinter-2023-analyzing</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-main.272</identifier>
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%0 Conference Proceedings
%T Analyzing Cognitive Plausibility of Subword Tokenization
%A Beinborn, Lisa
%A Pinter, Yuval
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F beinborn-pinter-2023-analyzing
%X Subword tokenization has become the de-facto standard for tokenization although comparative evaluations of their quality across languages are scarce. Existing evaluation studies focus on the effect of a tokenization algorithm on the performance in downstream tasks, or on engineering criteria such as the compression rate. We present a new evaluation paradigm that focuses on the cognitive plausibility of subword tokenization. We analyze the correlation of the tokenizer output with the reading time and accuracy of human responses on a lexical decision task. We compare three tokenization algorithms across several languages and vocabulary sizes. Our results indicate that the Unigram algorithm yields less cognitively plausible tokenization behavior and a worse coverage of derivational morphemes, in contrast with prior work.
%R 10.18653/v1/2023.emnlp-main.272
%U https://aclanthology.org/2023.emnlp-main.272/
%U https://doi.org/10.18653/v1/2023.emnlp-main.272
%P 4478-4486

Markdown (Informal)

[Analyzing Cognitive Plausibility of Subword Tokenization](https://aclanthology.org/2023.emnlp-main.272/) (Beinborn & Pinter, EMNLP 2023)

• Analyzing Cognitive Plausibility of Subword Tokenization (Beinborn & Pinter, EMNLP 2023)

ACL

• Lisa Beinborn and Yuval Pinter. 2023. Analyzing Cognitive Plausibility of Subword Tokenization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4478–4486, Singapore. Association for Computational Linguistics.