Summary of Team Ryu’s Submission to Sigmorphon 2024 Shared Task on Subword Tokenization, by Zilong Li
Team Ryu’s Submission to SIGMORPHON 2024 Shared Task on Subword Tokenization
by Zilong Li
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents team Ryu’s submission to the canceled SIGMORPHON 2024 shared task on subword tokenization, exploring whether morphological segmentation methods can be used in subword tokenizers. Two approaches are adopted: Morfessor, a statistical segmentation method, and a transformer-based sequence-to-sequence (seq2seq) segmentation model integrated into tokenizers. The results show that morphological segmentation can be as effective as common subword tokenizers. Additionally, the paper investigates how a tokenizer’s vocabulary affects language model performance, finding that a balanced token frequency distribution tends to work better. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers studied if using morphological segmentation in subword tokenization is helpful. They tried two ways: one based on statistics and another using transformers. The results showed that this approach can be as good as other methods used for subword tokenization. Also, the paper looked at how a tokenizer’s vocabulary affects language models. It found that having a balanced mix of frequent words helps. |
Keywords
» Artificial intelligence » Language model » Seq2seq » Token » Tokenization » Tokenizer » Transformer
