Summary of Using Language Models to Disambiguate Lexical Choices in Translation, by Josh Barua et al.
Using Language Models to Disambiguate Lexical Choices in Translation
by Josh Barua, Sanjay Subramanian, Kayo Yin, Alane Suhr
First submitted to arxiv on: 8 Nov 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 presents a novel dataset called DTAiLS, consisting of 1,377 sentence pairs that demonstrate cross-lingual concept variation when translating from English. The authors use this dataset to evaluate recent language models and neural machine translation systems, with the best-performing model being GPT-4, which achieves an accuracy range of 67-85% across languages. The study also demonstrates the potential of using language models to generate rules for lexical selection, improving the performance of weaker models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a special dataset called DTAiLS that shows how words can have different meanings in different languages when translating from English. They tested some of the latest AI language models and machine translation systems on this dataset and found that one model, GPT-4, performed well, getting around 67 to 85% of the answers correct across all the languages studied. The authors also showed how these language models can be used to create rules for choosing the right word in a target language, which helped weaker AI models perform better. |
Keywords
» Artificial intelligence » Gpt » Translation
