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- W3150702986 abstract "Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models as it provides multiple benefits. However, this process is solely based on pre-training data statistics, making it hard for the tokenizer to handle infrequent spellings. On the other hand, though robust to misspellings, pure character-level models often lead to unreasonably long sequences and make it harder for the model to learn meaningful words. To alleviate these challenges, we propose a character-based subword module (char2subword) that learns the subword embedding table in pre-trained models like BERT. Our char2subword module builds representations from characters out of the subword vocabulary, and it can be used as a drop-in replacement of the subword embedding table. The module is robust to character-level alterations such as misspellings, word inflection, casing, and punctuation. We integrate it further with BERT through pre-training while keeping BERT transformer parameters fixed–and thus, providing a practical method. Finally, we show that incorporating our module to mBERT significantly improves the performance on the social media linguistic code-switching evaluation (LinCE) benchmark." @default.
- W3150702986 created "2021-04-13" @default.
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- W3150702986 date "2021-01-01" @default.
- W3150702986 modified "2023-09-25" @default.
- W3150702986 title "Char2Subword: Extending the Subword Embedding Space Using Robust Character Compositionality" @default.
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- W3150702986 doi "https://doi.org/10.18653/v1/2021.findings-emnlp.141" @default.
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