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Similarity of Sentence Representations in Multilingual LMs: Resolving Conflicting Literature and Case Study of Baltic Languages

arXiv 2109.01207 ↗First posted 2 Sep 2021cs.CLcited by 6

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Abstract (arXiv)

Low-resource languages, such as Baltic languages, benefit from Large Multilingual Models (LMs) that possess remarkable cross-lingual transfer performance capabilities. This work is an interpretation and analysis study into cross-lingual representations of Multilingual LMs. Previous works hypothesized that these LMs internally project representations of different languages into a shared cross-lingual space. However, the literature produced contradictory results. In this paper, we revisit the prior work claiming that "BERT is not an Interlingua" and show that different languages do converge to a shared space in such language models with another choice of pooling strategy or similarity index. Then, we perform cross-lingual representational analysis for the two most popular multilingual LMs employing 378 pairwise language comparisons. We discover that while most languages share joint cross-lingual space, some do not. However, we observe that Baltic languages do belong to that shared space. The code is available at https://github.com/TartuNLP/xsim.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 2 Sep 2021.

Builds on

9 of 14 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  3. 2019Similarity of Neural Network Representations Revisitedcited by 1,755
  4. 2018XNLI: Evaluating Cross-lingual Sentence Representationscited by 915
  5. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 742
  6. 2018Insights on representational similarity in neural networks with canonical correlationcited by 324
  7. 2020XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generationcited by 222
  8. 2019Emerging Cross-lingual Structure in Pretrained Language Modelscited by 181
  9. 2021First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERTcited by 65

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Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 1.

6 papers.