Summary of Evaluating Subword Tokenization: Alien Subword Composition and Oov Generalization Challenge, by Khuyagbaatar Batsuren et al.
Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge
by Khuyagbaatar Batsuren, Ekaterina Vylomova, Verna Dankers, Tsetsuukhei Delgerbaatar, Omri Uzan, Yuval Pinter, Gábor Bella
First submitted to arxiv on: 20 Apr 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 proposed paper addresses the issue of subword tokenizers not respecting morpheme boundaries in current language models, which affects their performance. To solve this problem, the authors introduce a combined intrinsic-extrinsic evaluation framework for subword tokenization. The intrinsic evaluation uses a new UniMorph Labeller tool to classify tokenization as either morphological or alien. Extrinsic evaluation is performed through the Out-of-Vocabulary Generalization Challenge 1.0 benchmark, which consists of three text classification tasks. The findings show that alien tokenization leads to poorer generalizations compared to morphological tokenization for semantic compositionality of word meanings in all studied language models (ALBERT, BERT, RoBERTa, and DeBERTa). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to evaluate subword tokenizers. This is important because current tokenizers don’t follow the rules of words. The authors created a tool called UniMorph Labeller that helps identify good or bad tokenization. They also used a benchmark test with three tasks to see how well different language models work. The results show that using the wrong tokenizer can make it harder for language models to understand word meanings. |
Keywords
» Artificial intelligence » Bert » Generalization » Text classification » Tokenization » Tokenizer
