Team Ryu's Submission to SIGMORPHON 2024 Shared Task on Subword Tokenization

• URL: http://arxiv.org/abs/2410.17094v1
• Date: Sat, 19 Oct 2024 04:06:09 GMT
• Title: Team Ryu's Submission to SIGMORPHON 2024 Shared Task on Subword Tokenization
• Authors: Zilong Li,
• Abstract summary: My submission explores whether morphological segmentation methods can be used as a part of subword tokenizers. The prediction results show that morphological segmentation could be as effective as commonly used subword tokenizers. A tokenizer with a balanced token frequency distribution tends to work better.
• Score: 3.0023392750520883
• License:
• Abstract: This papers presents the submission of team Ryu to the canceled SIGMORPHON 2024 shared task on subword tokenization. My submission explores whether morphological segmentation methods can be used as a part of subword tokenizers. I adopt two approaches: the statistical segmentation method Morfessor and a transformer based sequence-to-sequence (seq2seq) segmentation model in tokenizers. The prediction results show that morphological segmentation could be as effective as commonly used subword tokenizers. Additionally, I investigate how a tokenizer's vocabulary influences the performance of language models. A tokenizer with a balanced token frequency distribution tends to work better. A balanced token vocabulary can be achieved by keeping frequent words as unique tokens.

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