mlsanityData to 7 Oct 2026

Multilingual and cross-lingual document classification: A meta-learning approach

arXiv 2101.11302 ↗First posted 27 Jan 2021cs.CLcited by 14

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Abstract (arXiv)

The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in limited-resource setting and demonstrate its effectiveness in two different settings: few-shot, cross-lingual adaptation to previously unseen languages; and multilingual joint training when limited target-language data is available during training. We conduct a systematic comparison of several meta-learning methods, investigate multiple settings in terms of data availability and show that meta-learning thrives in settings with a heterogeneous task distribution. We propose a simple, yet effective adjustment to existing meta-learning methods which allows for better and more stable learning, and set a new state of the art on several languages while performing on-par on others, using only a small amount of labeled data.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 30 of its 43 references that are in the map. First posted on arXiv 27 Jan 2021.

Builds on

30 of 43 references.

References · 30

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18 more
  1. 2018Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyondcited by 434
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 5; 2023: 3; 2024: 3; 2025: 0; 2026: 1.

14 papers.

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  1. 2023Zero-shot cross-lingual transfer language selection using linguistic similaritycited by 28
  2. 2022Meta Learning for Natural Language Processing: A Surveycited by 26
  3. 2021Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systemscited by 16
  4. 2022Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detectioncited by 8
  5. 2024A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospectscited by 6
  6. 2022Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generationcited by 4
  7. 2021Generalized Funnelling: Ensemble Learning and Heterogeneous Document Embeddings for Cross-Lingual Text Classificationcited by 1
  8. 2023Measuring Catastrophic Forgetting in Cross-Lingual Transfer Paradigms: Exploring Tuning Strategiescited by 1
  9. 2023Multilingual Sentence-Level Semantic Search using Meta-Distillation Learningcited by 1
  10. 2024A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content Detectioncited by 1
  11. 2022Persian Natural Language Inference: A Meta-learning approachcited by 0
  12. 2022Learning New Tasks from a Few Examples with Soft-Label Prototypescited by 0
2 more
  1. 2024Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learningcited by 0
  2. 2026MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoringcited by 0