Papers with cross-lingual adaptation
CLEAR: Cross-Lingual Enhancement in Retrieval via Reverse-training (2026.acl-long)
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| Challenge: | Existing multilingual embedding models often struggle to capture cross-lingual alignment during training. |
| Approach: | They propose a novel loss function that leverages an English passage as a bridge to strengthen alignments between target language and English. |
| Outcome: | The proposed model improves retrieval performance across cross-lingual scenarios while minimizing performance degradation in English. |
Cross-lingual Few-Shot Learning on Unseen Languages (2022.aacl-main)
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| Challenge: | Large pre-trained language models have demonstrated the ability to obtain good performance on downstream tasks with limited examples in resource-rich languages. |
| Approach: | They propose to use a downstream sentiment analysis task to analyze the effectiveness of several few-shot learning strategies across 12 languages, including 8 unseen languages, to compare results. |
| Outcome: | The proposed model, XLM-R, gives the best performance on a task with few examples in resource-rich languages. |
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification (2023.acl-short)
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| Challenge: | Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. |
| Approach: | They propose to use a dataset to classify sentiments for cross-domain adaptation for Nigerian and other African languages. |
| Outcome: | The proposed dataset compares the performance of cross-domain adaptation from Twitter domain and cross-lingual adaptation from English domain. |
Nearest Neighbour Few-Shot Learning for Cross-lingual Classification (2021.emnlp-main)
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| Challenge: | Existing pre-trained models can cause over-fitting when limited data are available. |
| Approach: | They propose to use a nearest-neighbor few-shot technique to improve cross-lingual adaptation using 16 distinct languages across two NLP tasks. |
| Outcome: | The proposed approach improves fine-tuning using only a handful of labeled samples in target locales and also generalizes across tasks. |
Multilingual and cross-lingual document classification: A meta-learning approach (2021.eacl-main)
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| Challenge: | Existing methods to document classification in low-resource languages are under-resourced . 6% of the world's languages are spoken, and many have inadequate resources . |
| Approach: | They propose a meta-learning approach to document classification in low-resource languages . they propose 'nuclear-shot' cross-lingual adaptation to previously unseen languages based on limited data . |
| Outcome: | The proposed method performs on-par on some languages while under-resourced in others. |
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages (2020.emnlp-main)
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| Challenge: | Existing mPLM-based methods focus on designing costly model pre-training while ignoring equally crucial downstream adaptation. |
| Approach: | They propose a meta graph learning method that extracts meta-knowledge from historical CLT experiences to learn to cross-lingual transfer. |
| Outcome: | The proposed method can learn to cross-lingual transfer by extracting meta-knowledge from historical CLT experiences (tasks) it can also capture intrinsic language relationships to explicitly guide cross-linguistic transfer. |
Large Margin Representation Learning for Robust Cross-lingual Named Entity Recognition (2025.acl-long)
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| Challenge: | Existing approaches to name entity recognition neglect distribution skewness and pseudo-label bias . despite promising results, current approaches neglect these problems . |
| Approach: | They propose a framework that optimizes an adaptively reweighted contrastive loss to handle class skewness and pseudo-label bias. |
| Outcome: | The proposed framework outperforms existing methods on multiple benchmarks. |
MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have advanced in many fields, but focus on English-centric models requires extensive data. |
| Approach: | They propose a method that leverages open-source static embedding models and up to 3 million tokens of code-switching data to facilitate the seamless transfer of embeddables to target languages. |
| Outcome: | The proposed method outperforms baseline and existing cross-lingual transfer methods in target languages. |