Summary of Unsupervised Morphological Tree Tokenizer, by Qingyang Zhu et al.
Unsupervised Morphological Tree Tokenizer
by Qingyang Zhu, Xiang Hu, Pengyu Ji, Wei Wu, Kewei Tu
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 introduces a new approach to tokenization in language modeling, which is crucial for segmenting text inputs into meaningful units. Conventional statistical tokenizers can disrupt word boundaries, losing semantic information. To address this issue, the authors propose a deep model that jointly encodes word structures and representations using MorphOverriding, ensuring indecomposability of morphemes. The model is trained with self-supervised objectives and demonstrates its effectiveness in inducing character-level structures aligned with morphological rules. The algorithm tokenizes words through vocabulary matching, outperforming BPE and WordPiece on both morphological segmentation tasks and language modeling tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to break down text into smaller parts called tokens. This is important for understanding language. Right now, most computer programs that analyze text can’t accurately separate words into their smallest meaningful parts. The authors suggest using a deep learning model to do this job better. They train the model with a special kind of self-teaching approach and show it works well on tasks like identifying morphemes (small units of meaning) in language. |
Keywords
» Artificial intelligence » Deep learning » Self supervised » Tokenization
