Papers with L1 pre-trained baseline model
Diversifying language models for lesser-studied languages and language-usage contexts: A case of second language Korean (2023.findings-emnlp)
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| Challenge: | Existing morpheme parsers/taggers do not work reliably and optimally for L2 data. |
| Approach: | They train a neural network model on varying L2 datasets and measure its morpheme parsing/POS tagging performance on L2 test sets. |
| Outcome: | The proposed model excels in domain-specific tokenization and POS tagging compared to the baseline model. |