Learning Mutually Informed Representations for Characters and Subwords (2024.findings-naacl)
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| Challenge: | Pretrained language models rely on subword tokenization to process text as a sequence of subwords. |
| Approach: | They propose a character-subword language model that integrates character and subword modalities into one model. |
| Outcome: | The proposed model outperforms its backbone language models on English sequence labeling and classification tasks. |
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