Summary of Optimal Synthesis Embeddings, by Roberto Santana and Mauricio Romero Sicre
Optimal synthesis embeddings
by Roberto Santana, Mauricio Romero Sicre
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 word embedding composition method is introduced, aiming to create a fair representation by minimizing the distance between the vector and its constituents. The method can work with static and contextualized word representations, generating sentence-level and set-level embeddings. Theoretical conditions for existence are derived, and the approach is evaluated in data augmentation and sentence classification tasks, exploring design choices of embeddings and composition methods. The method excels in probing tasks capturing simple linguistic features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to combine word meanings into one vector. They want this vector to be close to all the individual words that make it up. This works for single words, but also for sentences and groups of words. The researchers prove that their method works, then test it with different choices and find it’s great at detecting simple sentence features. |
Keywords
* Artificial intelligence * Classification * Data augmentation * Embedding
