LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913598994907136 |
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| author | Philippy, Fred Guo, Siwen Klein, Jacques Bissyandé, Tegawendé F. |
| author_facet | Philippy, Fred Guo, Siwen Klein, Jacques Bissyandé, Tegawendé F. |
| contents | Sentence embedding models play a key role in various Natural Language Processing tasks, such as in Topic Modeling, Document Clustering and Recommendation Systems. However, these models rely heavily on parallel data, which can be scarce for many low-resource languages, including Luxembourgish. This scarcity results in suboptimal performance of monolingual and cross-lingual sentence embedding models for these languages. To address this issue, we compile a relatively small but high-quality human-generated cross-lingual parallel dataset to train LuxEmbedder, an enhanced sentence embedding model for Luxembourgish with strong cross-lingual capabilities. Additionally, we present evidence suggesting that including low-resource languages in parallel training datasets can be more advantageous for other low-resource languages than relying solely on high-resource language pairs. Furthermore, recognizing the lack of sentence embedding benchmarks for low-resource languages, we create a paraphrase detection benchmark specifically for Luxembourgish, aiming to partially fill this gap and promote further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03331 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings Philippy, Fred Guo, Siwen Klein, Jacques Bissyandé, Tegawendé F. Computation and Language Artificial Intelligence Sentence embedding models play a key role in various Natural Language Processing tasks, such as in Topic Modeling, Document Clustering and Recommendation Systems. However, these models rely heavily on parallel data, which can be scarce for many low-resource languages, including Luxembourgish. This scarcity results in suboptimal performance of monolingual and cross-lingual sentence embedding models for these languages. To address this issue, we compile a relatively small but high-quality human-generated cross-lingual parallel dataset to train LuxEmbedder, an enhanced sentence embedding model for Luxembourgish with strong cross-lingual capabilities. Additionally, we present evidence suggesting that including low-resource languages in parallel training datasets can be more advantageous for other low-resource languages than relying solely on high-resource language pairs. Furthermore, recognizing the lack of sentence embedding benchmarks for low-resource languages, we create a paraphrase detection benchmark specifically for Luxembourgish, aiming to partially fill this gap and promote further research. |
| title | LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.03331 |