LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916422554222592 |
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| author | Adilazuarda, Muhammad Farid Cahyawijaya, Samuel Aji, Alham Fikri Winata, Genta Indra Purwarianti, Ayu |
| author_facet | Adilazuarda, Muhammad Farid Cahyawijaya, Samuel Aji, Alham Fikri Winata, Genta Indra Purwarianti, Ayu |
| contents | Pretrained language models (PLMs) have become remarkably adept at task and language generalization. Nonetheless, they often fail when faced with unseen languages. In this work, we present LinguAlchemy, a regularization method that incorporates various linguistic information covering typological, geographical, and phylogenetic features to align PLMs representation to the corresponding linguistic information on each language. Our LinguAlchemy significantly improves the performance of mBERT and XLM-R on low-resource languages in multiple downstream tasks such as intent classification, news classification, and semantic relatedness compared to fully finetuned models and displaying a high degree of unseen language generalization. We further introduce AlchemyScale and AlchemyTune, extension of LinguAlchemy which adjusts the linguistic regularization weights automatically, alleviating the need for hyperparameter search. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_06034 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization Adilazuarda, Muhammad Farid Cahyawijaya, Samuel Aji, Alham Fikri Winata, Genta Indra Purwarianti, Ayu Computation and Language Pretrained language models (PLMs) have become remarkably adept at task and language generalization. Nonetheless, they often fail when faced with unseen languages. In this work, we present LinguAlchemy, a regularization method that incorporates various linguistic information covering typological, geographical, and phylogenetic features to align PLMs representation to the corresponding linguistic information on each language. Our LinguAlchemy significantly improves the performance of mBERT and XLM-R on low-resource languages in multiple downstream tasks such as intent classification, news classification, and semantic relatedness compared to fully finetuned models and displaying a high degree of unseen language generalization. We further introduce AlchemyScale and AlchemyTune, extension of LinguAlchemy which adjusts the linguistic regularization weights automatically, alleviating the need for hyperparameter search. |
| title | LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2401.06034 |