Summary of Towards Cross-cultural Machine Translation with Retrieval-augmented Generation From Multilingual Knowledge Graphs, by Simone Conia et al.
Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphs
by Simone Conia, Daniel Lee, Min Li, Umar Farooq Minhas, Saloni Potdar, Yunyao Li
First submitted to arxiv on: 17 Oct 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 This paper tackles the challenge of cross-cultural translation, particularly when dealing with text that contains entity names. The authors introduce XC-Translate, a large-scale benchmark for machine translation focused on culturally-nuanced texts, and propose KG-MT, an end-to-end method that incorporates multilingual knowledge graphs into neural machine translation models using dense retrieval mechanisms. The results show that current systems struggle to translate entity-name containing texts, whereas KG-MT outperforms state-of-the-art approaches by a significant margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can understand and translate different languages better. It’s hard because words and names can have different meanings in different cultures. The authors created a special test for computer translation called XC-Translate, and they also came up with a new way to make computers translate more accurately using information from many languages. |
Keywords
» Artificial intelligence » Translation
