Summary of Retrieval-augmented Machine Translation with Unstructured Knowledge, by Jiaan Wang et al.
Retrieval-Augmented Machine Translation with Unstructured Knowledge
by Jiaan Wang, Fandong Meng, Yingxue Zhang, Jie Zhou
First submitted to arxiv on: 5 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 A novel approach, retrieval-augmented generation (RAG), enhances large language models (LLMs) for machine translation (MT). Existing RAG methods typically retrieve examples from paired MT corpora or domain-specific knowledge graphs to improve MT performance. However, this paper focuses on leveraging unstructured documents containing world knowledge, which may not be fully paired across languages. To address this, the authors introduce RAGtrans, a benchmark for training and evaluating LLMs’ retrieval-augmented MT ability. The dataset contains 79K MT samples, along with multilingual documents providing additional knowledge. A multi-task training method is proposed to teach LLMs to utilize information from these documents during translation. Experimental results demonstrate improved performance, with LLMs achieving 1.58-3.09 BLEU and 1.00-2.03 COMET scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning experts are developing new ways to make language models better at translating text between languages. They’re trying a new approach called retrieval-augmented generation (RAG) that uses lots of information from the internet to help. Right now, most RAG methods use special collections of translated text or dictionaries to improve translation quality. But what if they used all sorts of unorganized documents like books and articles? That’s what this paper is about! The authors created a new dataset called RAGtrans that has 79,000 examples of machine translation and also includes lots of documents in different languages. They’re trying to teach language models how to use this information to make better translations. So far, their approach seems to be working well, with some models getting up to 3 points better on a special scoring system. |
Keywords
» Artificial intelligence » Bleu » Machine learning » Multi task » Rag » Retrieval augmented generation » Translation
