---
title: "XLM-E: Cross-lingual Language Model Pre-training via ELECTRA"
description: "Cross-lingual encoders such as XLM-R are typically trained with masked language… 63.66 accuracy. 46 references, cited by 55 papers here."
authors: ["Zewen Chi", "Shaohan Huang", "Li Dong", "Shuming Ma", "Bo Zheng", "Saksham Singhal", "Payal Bajaj", "Xia Song", "Xian-Ling Mao", "Heyan Huang", "Furu Wei"]
year: 2022
venue: "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2022.acl-long.427/"
doi: "10.18653/v1/2022.acl-long.427"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chi-2022-xlm-e-cross-lingual-language/"
---
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# XLM-E: Cross-lingual Language Model Pre-training via ELECTRA

**Authors:** [Zewen Chi](https://md.research.elamur.ai/free/en/authors/zewen-chi/), [Shaohan Huang](https://md.research.elamur.ai/free/en/authors/shaohan-huang/), [Li Dong](https://md.research.elamur.ai/free/en/authors/li-dong/), [Shuming Ma](https://md.research.elamur.ai/free/en/authors/shuming-ma/), [Bo Zheng](https://md.research.elamur.ai/free/en/authors/bo-zheng/), [Saksham Singhal](https://md.research.elamur.ai/free/en/authors/saksham-singhal/), [Payal Bajaj](https://md.research.elamur.ai/free/en/authors/payal-bajaj/), [Xia Song](https://md.research.elamur.ai/free/en/authors/xia-song/), [Xian-Ling Mao](https://md.research.elamur.ai/free/en/authors/xian-ling-mao/), [Heyan Huang](https://md.research.elamur.ai/free/en/authors/heyan-huang/) and [Furu Wei](https://md.research.elamur.ai/free/en/authors/furu-wei/) · **Year:** 2022 · **Venue:** [Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2022](https://md.research.elamur.ai/free/en/venues/acl/2022/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.acl-long.427/) · [PDF](https://aclanthology.org/2022.acl-long.427.pdf) · [DOI 10.18653/v1/2022.acl-long.427](https://doi.org/10.18653/v1/2022.acl-long.427) · [OpenAlex](https://openalex.org/W3175898847)

**Citations:** 71 citations (OpenAlex, 2026-10-14) · [55](https://md.research.elamur.ai/free/en/papers/chi-2022-xlm-e-cross-lingual-language/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Cross-lingual encoders such as XLM-R are typically trained with masked language modeling, sometimes extended with parallel-data objectives like TLM, and this demands very large compute budgets. The authors adapt ELECTRA to the multilingual setting. In ELECTRA, the encoder learns as a discriminator that spots tokens a small generator has substituted. XLM-E combines two such objectives. Multilingual replaced token detection (MRTD) works on monolingual text from CC-100. Translation replaced token detection (TRTD) corrupts a concatenated translation pair, and the discriminator flags altered tokens in both languages. The generator is trained jointly using MLM and TLM losses. Self-attention also gets a new gated relative position bias, which lets positional bias depend on token content. On the XTREME benchmark, the Base-size model is competitive with or better than strong baselines while using roughly 1% of XLM-R's pre-training compute. Probing indicates its upper layers produce representations that are better aligned across languages. The work shows that capable multilingual encoders can be built at a fraction of the usual cost, which matters for groups with modest hardware.

## Key points
- Pre-training data: CC-100 (100 languages, from CommonCrawl) for MRTD. Parallel data for TRTD comes from MultiUN, IIT Bombay, OPUS, WikiMatrix and CCAligned. Languages are rebalanced by exponent sampling with α = 0.7.
- The overall loss sums MLM and TLM for the generator with weighted MRTD and TRTD for the discriminator. Only the discriminator, a 12-layer Base Transformer with hidden size 768, is kept for fine-tuning. Span masking was tested but hurt generator accuracy, so uniform masking is used.
- Efficiency: XLM-E reaches comparable XNLI accuracy about 130× faster than an in-house XLM-R + TLM trained on the same corpora and code base. Across seven XTREME datasets it needs about 1% of XLM-R's compute.
- On word alignment, XLM-E peaks at layer 9 with 63.66 accuracy, versus XLM-R's best of 54.42 at layer 7. At layer 10 XLM-E still scores 57.14, while XLM-R drops to 43.34. Alignment error rate follows the same pattern.
- On cross-lingual transfer gap, XLM-E has the lowest gap among compared models only on PAWS-X. Its gaps on XQuAD, MLQA, TyDiQA and XNLI are reasonably small but not the best. Code is released at github.com/microsoft/unilm.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) — Company, Redmond, United States · 240 works · 123,537 citations
- [Beijing Institute of Technology](https://md.research.elamur.ai/free/en/universities/beijing-institute-of-technology/) — University, Beijing, China · 15 works · 3,480 citations

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## Cite

Cite as: Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang and Furu Wei. 2022. XLM-E: Cross-lingual Language Model Pre-training via ELECTRA. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{chi2022xlm,
  title = {{XLM-E: Cross-lingual Language Model Pre-training via ELECTRA}},
  author = {Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang and Furu Wei},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2022},
  doi = {10.18653/v1/2022.acl-long.427},
  url = {https://doi.org/10.18653/v1/2022.acl-long.427}
}
```

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