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arXiv · 2310.13348

Analyzing Cognitive Plausibility of Subword Tokenization

Lisa Beinborn·Yuval Pinter
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Abstract

Subword tokenization has become the de-facto standard for tokenization, although comparative evaluations of subword vocabulary 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 response time and accuracy of human performance on a lexical decision task. We compare three tokenization algorithms across several languages and vocabulary sizes. Our results indicate that the UnigramLM algorithm yields less cognitively plausible tokenization behavior and a worse coverage of derivational morphemes, in contrast with prior work.

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Lisa Beinborn, Yuval Pinter. 2023-10-20. Analyzing Cognitive Plausibility of Subword Tokenization. https://arxiv.org/abs/2310.13348

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