Summary of Mothello: When Do Cross-lingual Representation Alignment and Cross-lingual Transfer Emerge in Multilingual Models?, by Tianze Hua et al.
mOthello: When Do Cross-Lingual Representation Alignment and Cross-Lingual Transfer Emerge in Multilingual Models?
by Tianze Hua, Tian Yun, Ellie Pavlick
First submitted to arxiv on: 18 Apr 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 The paper investigates the factors contributing to the learning of a language-neutral representation in multilingual models during pretraining. It proposes a synthetic task, Multilingual Othello (mOthello), as a testbed to explore this question. The results show that naive multilingual pretraining fails to learn a language-neutral representation across all input languages. However, introducing “anchor tokens” helps align the cross-lingual representation. Moreover, learning a language-neutral representation alone is not sufficient for cross-lingual transfer. Based on these findings, the paper proposes a novel approach – multilingual pretraining with unified output space – that induces the learning of language-neutral representation and facilitates cross-lingual transfer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how multilingual models learn to work across different languages during training. It creates a special task called Multilingual Othello (mOthello) to test how well these models can learn from multiple languages. The results show that some approaches are better than others, and that adding special “anchor tokens” helps the models understand language differences. However, just learning about language differences isn’t enough for the models to work across all languages. Based on this discovery, the paper suggests a new way of training multilingual models that combines the benefits of different approaches. |
Keywords
» Artificial intelligence » Pretraining
