Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varieties
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914533737496576 |
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| author | Kim, Jinju Jung, Haeji Roh, Youjeong Ko, Jong Hwan Mortensen, David R. |
| author_facet | Kim, Jinju Jung, Haeji Roh, Youjeong Ko, Jong Hwan Mortensen, David R. |
| contents | Low-resource language varieties used by specific groups remain neglected in the development of Multilingual Language Models. A great deal of cross-lingual research focuses on inter-lingual language transfer which strives to align allied varieties and minimize differences between them. However, for low-resource varieties, linguistic dissimilarity is also an important cue allowing generalization to unseen varieties. Unlike prior approaches, we propose a two-stage Language Generalization framework that focuses on capturing variety-specific cues while also exploiting rich overlap offered by high-resource source variety. First, we propose TOPPing, a source-selection method specifically designed for low-resource varieties. Second, we suggest a lightweight VACAI-Bowl architecture that learns variety-specific attributes with one branch while a parallel branch captures variety-invariant attributes using adversarial training. We evaluate our framework on structural prediction tasks, which are among the few tasks available, as proxy for performance on other downstream tasks. Using VACAI-Bowl with TOPPing yields an average 54.62% improvement in the dependency parsing task, which serves as a proxy for performance on other downstream tasks across 10 low-resource varieties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04500 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varieties Kim, Jinju Jung, Haeji Roh, Youjeong Ko, Jong Hwan Mortensen, David R. Computation and Language Artificial Intelligence Low-resource language varieties used by specific groups remain neglected in the development of Multilingual Language Models. A great deal of cross-lingual research focuses on inter-lingual language transfer which strives to align allied varieties and minimize differences between them. However, for low-resource varieties, linguistic dissimilarity is also an important cue allowing generalization to unseen varieties. Unlike prior approaches, we propose a two-stage Language Generalization framework that focuses on capturing variety-specific cues while also exploiting rich overlap offered by high-resource source variety. First, we propose TOPPing, a source-selection method specifically designed for low-resource varieties. Second, we suggest a lightweight VACAI-Bowl architecture that learns variety-specific attributes with one branch while a parallel branch captures variety-invariant attributes using adversarial training. We evaluate our framework on structural prediction tasks, which are among the few tasks available, as proxy for performance on other downstream tasks. Using VACAI-Bowl with TOPPing yields an average 54.62% improvement in the dependency parsing task, which serves as a proxy for performance on other downstream tasks across 10 low-resource varieties. |
| title | Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varieties |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.04500 |