Summary of Luxembedder: a Cross-lingual Approach to Enhanced Luxembourgish Sentence Embeddings, by Fred Philippy et al.
LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings
by Fred Philippy, Siwen Guo, Jacques Klein, Tegawendé F. Bissyandé
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an enhanced sentence embedding model called LuxEmbedder, designed for Luxembourgish, a low-resource language. The model relies on human-generated parallel data to overcome the scarcity of parallel data in Luxembourgish, which is crucial for monolingual and cross-lingual sentence embeddings. Additionally, the authors demonstrate that including low-resource languages in parallel training datasets can be beneficial for other low-resource languages. They also introduce a paraphrase detection benchmark specifically for Luxembourgish, aiming to promote further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Luxembourgish is a language with limited resources. Researchers have created an enhanced sentence embedding model called LuxEmbedder to help with this issue. The model uses human-generated data that is similar in different languages. This helps overcome the lack of parallel data in Luxembourgish, which is important for understanding sentences in this language and comparing it to other languages. The authors also show that including low-resource languages like Luxembourgish in training datasets can be helpful for other languages with limited resources. |
Keywords
» Artificial intelligence » Embedding
