Academic

Why Better Cross-Lingual Alignment Fails for Better Cross-Lingual Transfer: Case of Encoders

arXiv:2603.18863v1 Announce Type: new Abstract: Better cross-lingual alignment is often assumed to yield better cross-lingual transfer. However, explicit alignment techniques -- despite increasing embedding similarity -- frequently fail to improve token-level downstream performance. In this work, we show that this mismatch arises because alignment and downstream task objectives are largely orthogonal, and because the downstream benefits from alignment vary substantially across languages and task types. We analyze four XLM-R encoder models aligned on different language pairs and fine-tuned for either POS Tagging or Sentence Classification. Using representational analyses, including embedding distances, gradient similarities, and gradient magnitudes for both task and alignment losses, we find that: (1) embedding distances alone are unreliable predictors of improvements (or degradations) in task performance and (2) alignment and task gradients are often close to orthogonal, indicating th

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Yana Veitsman, Yihong Liu, Hinrich Sch\"utze · · 1 min read · 6 views
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arXiv:2603.18863v1 Announce Type: new Abstract: Better cross-lingual alignment is often assumed to yield better cross-lingual transfer. However, explicit alignment techniques -- despite increasing embedding similarity -- frequently fail to improve token-level downstream performance. In this work, we show that this mismatch arises because alignment and downstream task objectives are largely orthogonal, and because the downstream benefits from alignment vary substantially across languages and task types. We analyze four XLM-R encoder models aligned on different language pairs and fine-tuned for either POS Tagging or Sentence Classification. Using representational analyses, including embedding distances, gradient similarities, and gradient magnitudes for both task and alignment losses, we find that: (1) embedding distances alone are unreliable predictors of improvements (or degradations) in task performance and (2) alignment and task gradients are often close to orthogonal, indicating that optimizing one objective may contribute little to optimizing the other. Taken together, our findings explain why ``better'' alignment often fails to translate into ``better'' cross-lingual transfer. Based on these insights, we provide practical guidelines for combining cross-lingual alignment with task-specific fine-tuning, highlighting the importance of careful loss selection.

Executive Summary

This article challenges the long-held assumption that better cross-lingual alignment necessarily leads to improved cross-lingual transfer. Through a comprehensive analysis of four XLM-R encoder models fine-tuned for POS Tagging and Sentence Classification, the authors reveal that alignment and downstream task objectives are largely orthogonal, making it unreliable to predict task performance based on embedding distances alone. As a result, better alignment does not always translate to better cross-lingual transfer. The study provides valuable insights into the intricacies of cross-lingual alignment and task-specific fine-tuning, highlighting the importance of careful loss selection. The findings have significant implications for the development of more effective cross-lingual transfer models, particularly in scenarios where languages and task types vary substantially.

Key Points

  • ▸ Better cross-lingual alignment does not necessarily lead to improved cross-lingual transfer.
  • ▸ Alignment and downstream task objectives are largely orthogonal, making it unreliable to predict task performance based on embedding distances alone.
  • ▸ The importance of careful loss selection in cross-lingual alignment and task-specific fine-tuning is emphasized.

Merits

Strength of Analytical Approach

The authors employ a comprehensive methodology, combining representational analyses and gradient similarities to provide a nuanced understanding of the relationship between cross-lingual alignment and downstream task performance.

Demerits

Limitation of Generalizability

The study is limited in its generalizability, as it focuses on a specific set of encoder models and downstream tasks, which may not be representative of all cross-lingual transfer scenarios.

Expert Commentary

The article provides a thorough and insightful analysis of the relationship between cross-lingual alignment and downstream task performance. By highlighting the limitations of better alignment and emphasizing the importance of careful loss selection, the authors offer valuable guidance for the development of more effective cross-lingual transfer models. However, the study's focus on a specific set of encoder models and downstream tasks may limit its generalizability to broader cross-lingual transfer scenarios. To further advance this line of research, future studies should aim to generalize these findings to a wider range of models and tasks.

Recommendations

  • ✓ Future studies should investigate the application of the findings to other encoder models and downstream tasks to enhance the study's generalizability.
  • ✓ Researchers should explore the development of more effective cross-lingual alignment techniques that account for the complexities of varying languages and task types.

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