Tokenization Tractability for Human and Machine Learning Model: An Annotation Study
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Is tractable tokenization for humans also tractable for machine learning models? This study investigates relations between tractable tokenization for humans (e.g., appropriateness and readability) and one for models of machine learning (e.g., performance on an NLP task). We compared six tokenization methods on the Japanese commonsense question-answering dataset (JCommmonsenseQA in JGLUE). We tokenized question texts of the QA dataset with different tokenizers and compared the performance of human annotators and machine-learning models. Besides,we analyze relationships among the performance, appropriateness of tokenization, and response time to questions. This paper provides a quantitative investigation result that shows the tractable tokenizations for humans and machine learning models are not necessarily the same as each other.
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