Summary of Multilingual De-duplication Strategies: Applying Scalable Similarity Search with Monolingual & Multilingual Embedding Models, by Stefan Pasch et al.
Multilingual De-Duplication Strategies: Applying scalable similarity search with monolingual & multilingual embedding models
by Stefan Pasch, Dimitirios Petridis, Jannic Cutura
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 explores a novel approach to deduplicating multilingual textual data using advanced NLP tools, specifically comparing two-step methods involving translation to English followed by embedding with mpnet, and a multilingual embedding model (distiluse). The results show that the two-step approach achieves a higher F1 score (82% vs. 60%) for less widely used languages, which can be further improved up to 89% by leveraging expert rules based on domain knowledge. Additionally, the methodology highlights limitations related to token length constraints and computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to get rid of duplicate texts written in different languages. It compares two methods: one that translates all the text into English first, then uses a special tool called mpnet, and another method that uses a multilingual tool right away. The results show that the translation-based method works better for less common languages. By using expert rules to help with this process, it can even work up to 89% accurately! However, there are some limitations, like how long each text is and how much computing power is needed. |
Keywords
» Artificial intelligence » Embedding » F1 score » Nlp » Token » Translation
