Papers with cross-lingual transfer methods
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking (2021.acl-short)
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| Challenge: | Existing work on transferring domain-specific knowledge from a pretraining model to a resource-poor language is limited to English . a novel cross-lingual biomedical entity linking task is proposed to improve this capability. |
| Approach: | They propose a cross-lingual biomedical entity linking task and establish a new benchmark spanning 10 typologically diverse languages. |
| Outcome: | The proposed methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language and without any parallel data. |
Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media (2023.emnlp-main)
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| Challenge: | Claim span identification (CSI) is an important step in fact-checking pipelines . despite its importance to journalists and fact-seekers, it remains a understudied problem . |
| Approach: | They propose to use social media claims to identify text segments that contain a check-worthy claim or assertion in a social media post. |
| Outcome: | The proposed dataset outperforms other cross-lingual transfer methods on multiple languages. |
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition (2022.emnlp-main)
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David Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba Alabi, Shamsuddeen Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, Fatoumata Kabore, Chris Chinenye Emezue, Anuoluwapo Aremu, Perez Ogayo, Catherine Gitau, Edwin Munkoh-Buabeng, Victoire Memdjokam Koagne, Allahsera Auguste Tapo, Tebogo Macucwa, Vukosi Marivate, Mboning Tchiaze Elvis, Tajuddeen Gwadabe, Tosin Adewumi, Orevaoghene Ahia, Joyce Nakatumba-Nabende, Neo Lerato Mokono, Ignatius Ezeani, Chiamaka Chukwuneke, Mofetoluwa Oluwaseun Adeyemi, Gilles Quentin Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu, Dietrich Klakow
| Challenge: | Existing studies on named entity recognition methods for African languages focus on English as the source language, but there is evidence that it is not the best for low-resource languages. |
| Approach: | They propose to use human-annotated datasets to analyze named entity recognition tasks in 20 African languages to test whether they are effective. |
| Outcome: | The proposed method improves zero-shot F1 scores by 14% over 20 languages compared to using English . |
Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog (N19-1)
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| Challenge: | a lack of multilingual training data has hindered development of conversational AI models for task-oriented tasks . a new data set of 57k annotated utterances in english, spanish, and Thai is used to evaluate cross-lingual methods . |
| Approach: | They present a data set of 57k annotated utterances in English, Spanish and Thai . they evaluate three different cross-lingual transfer methods to identify user intents and slots . |
| Outcome: | The proposed model outperforms existing methods in English, Spanish and Thai . the proposed model is based on training data from three languages . |
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment (2023.findings-emnlp)
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| Challenge: | Existing cross-lingual transfer methods that use labeled data and linguistic resources would consume excessive resources for a large number of languages. |
| Approach: | They propose a parameter-efficient cross-lingual transfer learning framework that utilizes a translation-based alignment method to mitigate multilingual disparities. |
| Outcome: | The proposed framework reduces disparities among languages and improves cross-lingual transfer results in low-resource scenarios while keeping and fine-tuning only a small number of parameters. |
Enhancing Cross-lingual Transfer via Phonemic Transcription Integration (2023.findings-acl)
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| Challenge: | Previous cross-lingual transfer methods are limited to orthographic representation learning via textual scripts. |
| Approach: | They propose a phonemic transcription framework that incorporates phonemic translations as an additional linguistic modality beyond the orthographic transcriptions for cross-lingual transfer. |
| Outcome: | The proposed framework captures local one-to-one alignment between two different modalities and integrates bilingual dictionaries into multilingual contexts. |
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. |
Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study (2021.emnlp-main)
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| Challenge: | Unlike most of the previous work focusing on the English language, this paper focuses on the Chinese ORL task. |
| Approach: | They propose to use a standard English MPQA dataset to construct a Chinese ORL dataset and investigate the effectiveness of cross-lingual transfer methods. |
| Outcome: | The proposed method is able to detect and improve the performance of the proposed method in Chinese. |
Language Fusion for Parameter-Efficient Cross-lingual Transfer (2025.acl-long)
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| Challenge: | Limited availability of multilingual text corpora for pretraining results in poor performance on downstream tasks due to undertrained representation spaces for languages other than English. |
| Approach: | They propose a method that integrates source and target language representations within low-rank (LoRA) adapters using lightweight linear transformations to enhance representation quality and transfer performance for languages other than English. |
| Outcome: | The proposed method improves representation quality and performance for languages other than English while maintaining parameter efficiency. |