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Is tractable tokenization for humans also tractable for machine learningmodels? This study investigates relations between tractable tokenization forhumans (e.g., appropriateness and readability) and one for models of machinelearning (e.g., performance on an NLP task). We compared six tokenizationmethods on the Japanese commonsense question-answering dataset (JCommmonsenseQAin JGLUE). We tokenized question texts of the QA dataset with differenttokenizers and compared the performance of human annotators andmachine-learning models. Besides,we analyze relationships among theperformance, appropriateness of tokenization, and response time to questions.This paper provides a quantitative investigation result that shows thetractable tokenizations for humans and machine learning models are notnecessarily the same as each other.

Tokenization Tractability for Human and Machine Learning Model: An Annotation Study