Summary of Exploring Alignment in Shared Cross-lingual Spaces, by Basel Mousi and Nadir Durrani and Fahim Dalvi and Majd Hawasly and Ahmed Abdelali
Exploring Alignment in Shared Cross-lingual Spaces
by Basel Mousi, Nadir Durrani, Fahim Dalvi, Majd Hawasly, Ahmed Abdelali
First submitted to arxiv on: 23 May 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 This paper investigates the alignment of linguistic concepts across languages in multilingual embeddings, aiming to quantify the degree of overlap between high-dimensional representations. The authors employ clustering to identify latent concepts within three multilingual models (mT5, mBERT, and XLM-R) and analyze their performance on three downstream tasks (Machine Translation, Named Entity Recognition, and Sentiment Analysis). The study reveals that deeper layers in the network demonstrate increased cross-lingual alignment due to language-agnostic concepts, fine-tuning enhances alignment within the latent space, and task-specific calibration explains the emergence of zero-shot capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper looks at how different languages relate to each other in a computer program that can understand many languages. The researchers used special techniques to find patterns in the program’s output and found that some parts are more similar across languages than others. They also looked at how well the program performed on certain tasks like translating text or identifying important words. The results showed that the program gets better at understanding language relationships as it goes deeper into its calculations, and that fine-tuning the program makes it even better. |
Keywords
» Artificial intelligence » Alignment » Clustering » Fine tuning » Latent space » Named entity recognition » Translation » Zero shot
