Papers with tokenization approaches
Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation (2022.tacl-1)
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| Challenge: | End-to-end neural models have replaced the traditional pipeline and require an explicit tokenization step. |
| Approach: | They propose a neural encoder that operates directly on character sequences without explicit tokenization or vocabulary and a pre-training strategy that optionally uses subwords as a soft inductive bias. |
| Outcome: | The proposed model outperforms a comparable mBert model on a multilingual benchmark by 5.7 F1 on the TyDi QA benchmark. |
Tokenization Is More Than Compression (2024.emnlp-main)
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| Challenge: | Existing tokenization approaches like Byte-Pair Encoding (BPE) have been suggested that their effectiveness stems from their ability to condense text into a relatively small number of tokens. |
| Approach: | They propose a tokenizer that segments a document’s text into the minimum number of tokens for a given vocabulary and propose fewer tokens to improve downstream performance. |
| Outcome: | The proposed tokenizers can initialize vocabulary construction and pre-tokenization, and the results show that fewer tokens lead to better performance. |
Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation (2023.findings-emnlp)
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| Challenge: | Text normalization is a range of tasks that consist in replacing non-standard spellings with their standard equivalents. |
| Approach: | They introduce dialect-to-standard normalization as a sentence-level character transduction task and provide a large-scale analysis of these methods. |
| Outcome: | The proposed model performs best for Finnish, Swiss German and Slovene while the pre-trained model using full sentences performs the best for Norwegian. |