Minimax and Neyman–Pearson Meta-Learning for Outlier Languages (2021.findings-acl)
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| Challenge: | Model-agnostic meta-learning (MAML) is a strategy to learn resource-poor languages in a sample-efficient fashion. |
| Approach: | They propose a model-agnostic meta-learning strategy that minimizes the expected risk across languages with a uniform prior . they propose 'minimax' and 'neyman-pearson' models that constrain the risk in each language to a maximum threshold. |
| Outcome: | The proposed model reduces the maximum risk across languages while constraining the risk in each language to a maximum threshold. |
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| Challenge: | Existing methods to learn general representations of text can achieve sub-optimal performance in low-resource scenarios. |
| Approach: | They propose to use language model pre-training and multi-task learning to learn robust representations but these methods can achieve sub-optimal performance in low-resource scenarios. |
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Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing (2022.acl-long)
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Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova
| Challenge: | Meta-learning can help overcome resource scarcity in cross-lingual NLP problems . pre-training of models requires large annotated training sets for the task at hand . |
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AdaptFlow: Adaptive Workflow Optimization via Meta-Learning (2025.findings-emnlp)
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Runchuan Zhu, Bowen Jiang, Lingrui Mei, Fangkai Yang, Lu Wang, Haoxiang Gao, Fengshuo Bai, Pu Zhao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
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Meta-Learning for Low-Resource Neural Machine Translation (D18-1)
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| Challenge: | In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm for low-resource neural machine translation (NMT). |
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Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets (2024.lrec-main)
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| Challenge: | Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities for downstream tasks such as Named Entity Recognition (NER) challenges persist in MLLM implementations that are not cross-linguistically robust. |
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X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering (2021.naacl-main)
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| Challenge: | Multilingual models have gained popularity for their zero-shot cross-lingual transfer learning capabilities, but their generalization ability is inconsistent for typologically diverse languages. |
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Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation (2022.findings-acl)
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| Challenge: | Existing approaches to learn shareable structures from low-resource languages are limited in the zero-shot setting. |
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Decoupling Generalization and Adaptation in Meta-Learning for Large Language Models (2026.acl-short)
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| Challenge: | Adapting large language models to specific downstream tasks requires multi-step fine-tuning with substantial training data, incurring significant computational overhead. |
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Zero-Shot Cross-Lingual Transfer with Meta Learning (2020.emnlp-main)
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| Challenge: | There are more than 7,000 languages spoken in the world, over 90 of which have more than 10 million native speakers each. |
| Approach: | They propose to use meta-learning to train a model on multiple languages at the same time . they use standard supervised, zero-shot cross-lingual, and few-shot crosses-lingual settings for different natural language understanding tasks. |
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Meta-Learning a Cross-lingual Manifold for Semantic Parsing (2023.tacl-1)
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| Challenge: | Recent work has found success with machine translation or zero-shot methods . however, these approaches can struggle to model how native speakers ask questions . |
| Approach: | They propose a meta-learning algorithm to leverage minimal annotated examples in new languages for few-shot cross-lingual semantic parsing. |
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