Summary of Discovering Low-rank Subspaces For Language-agnostic Multilingual Representations, by Zhihui Xie et al.
Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations
by Zhihui Xie, Handong Zhao, Tong Yu, Shuai Li
First submitted to arxiv on: 11 Jan 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 This paper presents a novel approach for projecting away language-specific factors from multilingual embedding spaces, allowing for more effective semantic transfer between languages. Specifically, the authors identify a low-rank subspace that encodes information irrelevant to semantics and develop an unsupervised method using singular value decomposition to find this subspace. By projecting original embeddings into the null space, the model achieves language agnosticism without requiring fine-tuning. The approach is evaluated on various tasks, including language-agnostic QA retrieval, with consistent improvements over commonly used multilingual language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make language models work better across different languages. It’s like trying to remove extra noise from the model so it can focus on understanding the meaning of words and sentences. The authors found a way to do this by identifying parts of the language model that don’t really matter for understanding, and then removing those parts. This makes the model more “language-agnostic”, which means it’s not just good at one language but many languages. The results show that this method works well on different tasks like answering questions about text. |
Keywords
* Artificial intelligence * Embedding * Fine tuning * Language model * Semantics * Unsupervised
