# On the Language Neutrality of Pre-trained Multilingual Representations

arXiv: https://arxiv.org/abs/2004.05160 · HTML: https://mlsanity.com/paper/2004.05160/

## Key facts

- Cited by 85 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 30 of 85)

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- [Understanding Cross-Lingual Alignment -- A Survey](https://mlsanity.com/paper/2404.06228/) (2024)
- [Enhancing Cross-lingual Transfer by Manifold Mixup](https://mlsanity.com/paper/2205.04182/) (2022)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [Discovering Language-neutral Sub-networks in Multilingual Language Models](https://mlsanity.com/paper/2205.12672/) (2022)
- [Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://mlsanity.com/paper/2601.14004/) (2026)
- [Match the Script, Adapt if Multilingual: Analyzing the Effect of Multilingual Pretraining on Cross-lingual Transferability](https://mlsanity.com/paper/2203.10753/) (2022)
- [Text Embedding Inversion Security for Multilingual Language Models](https://mlsanity.com/paper/2401.12192/) (2024)
- [Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models](https://mlsanity.com/paper/2410.11718/) (2024)
- [mOthello: When Do Cross-Lingual Representation Alignment and Cross-Lingual Transfer Emerge in Multilingual Models?](https://mlsanity.com/paper/2404.12444/) (2024)
- [Beneath the Surface of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in LLMs](https://mlsanity.com/paper/2408.10646/) (2024)
- [Language Representation Projection: Can We Transfer Factual Knowledge across Languages in Multilingual Language Models?](https://mlsanity.com/paper/2311.03788/) (2023)
- [Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations](https://mlsanity.com/paper/2401.05792/) (2024)
- [Improving Word Translation via Two-Stage Contrastive Learning](https://mlsanity.com/paper/2203.08307/) (2022)
- [Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure](https://mlsanity.com/paper/2203.08430/) (2022)
- [The Hidden Space of Transformer Language Adapters](https://mlsanity.com/paper/2402.13137/) (2024)
- [Do Multilingual Language Models Capture Differing Moral Norms?](https://mlsanity.com/paper/2203.09904/) (2022)
- [How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning](https://mlsanity.com/paper/2305.13286/) (2023)
- [MELA: Multilingual Evaluation of Linguistic Acceptability](https://mlsanity.com/paper/2311.09033/) (2023)
- [Identifying the Correlation Between Language Distance and Cross-Lingual Transfer in a Multilingual Representation Space](https://mlsanity.com/paper/2305.02151/) (2023)
- [Analyzing Gender Representation in Multilingual Models](https://mlsanity.com/paper/2204.09168/) (2022)

## Abstract (arXiv)

Multilingual contextual embeddings, such as multilingual BERT and XLM-RoBERTa, have proved useful for many multi-lingual tasks. Previous work probed the cross-linguality of the representations indirectly using zero-shot transfer learning on morphological and syntactic tasks. We instead investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings, which are explicitly trained for language neutrality. Contextual embeddings are still only moderately language-neutral by default, so we propose two simple methods for achieving stronger language neutrality: first, by unsupervised centering of the representation for each language and second, by fitting an explicit projection on small parallel data. Besides, we show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences without using parallel data.