Summary of Multi-task Contrastive Learning For 8192-token Bilingual Text Embeddings, by Isabelle Mohr et al.
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings
by Isabelle Mohr, Markus Krimmel, Saba Sturua, Mohammad Kalim Akram, Andreas Koukounas, Michael Günther, Georgios Mastrapas, Vinit Ravishankar, Joan Fontanals Martínez, Feng Wang, Qi Liu, Ziniu Yu, Jie Fu, Saahil Ognawala, Susana Guzman, Bo Wang, Maximilian Werk, Nan Wang, Han Xiao
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 We present a groundbreaking collection of advanced bilingual text embedding models optimized for English and another target language. These innovative models can handle lengthy texts with up to 8192 tokens, making them highly versatile for various natural language processing tasks such as text retrieval, clustering, and semantic textual similarity (STS) calculations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a set of cutting-edge bilingual text embedding models that work well with English and another target language. These new models can handle long texts and are great for many tasks like finding the right information, grouping similar texts together, and measuring how similar two pieces of text are to each other. |
Keywords
» Artificial intelligence » Clustering » Embedding » Natural language processing
