Papers with Multilingual pre-
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension (2022.acl-long)
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| Challenge: | Existing methods to zero-shot transfer knowledge from rich-resource to low-resourced languages are limited due to linguistic discrepancies in different languages. |
| Approach: | They propose a multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model to disassociate semantics from syntax in models learned by multilingual pre-trained models. |
| Outcome: | The proposed model disassociates semantics from syntax in multilingual models. |
Discovering Representation Sprachbund For Multilingual Pre-Training (2021.findings-emnlp)
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| Challenge: | Existing models perform poorly on many languages and cross-lingual tasks due to typological differences and contradictions between some languages. |
| Approach: | They propose to pre-train multilingual pre-trained models to handle cross-lingual tasks in one model. |
| Outcome: | The proposed model improves performance on cross-lingual tasks compared to baselines on multiple languages . |
Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension (2020.acl-main)
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| Challenge: | Existing approaches to improve machine reading comprehension performance on low resource languages are limited due to the lack of sufficient training data. |
| Approach: | They propose to use a mixed MRC task to translate the question to other languages and build cross-lingual question-passage pairs. |
| Outcome: | The proposed task improves on two cross-lingual MRC datasets. |
Lifting the Curse of Multilinguality by Pre-training Modular Transformers (2022.naacl-main)
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| Challenge: | Recent work on multilingual pre-trained models has focused on pre-training transformers on concatenated corpora of a large number of languages. |
| Approach: | They propose a language-specific module approach that allows for more languages to be trained post-hoc. |
| Outcome: | The proposed model can be pre-trained on multiple languages with no drop in performance . |
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure (2022.acl-long)
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| Challenge: | Multilingual pre-trained language models have shown impressive cross-lingual ability. |
| Approach: | They argue that cross-language ability comes from commonality between languages . they create an artificial language by modifying property in source language . |
| Outcome: | The proposed model can be implemented in multilingual and low-resource language scenarios without cross-lingual supervision or aligned data. |
Language Anisotropic Cross-Lingual Model Editing (2023.findings-acl)
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| Challenge: | Existing work studies monolingual model editing, which lacks cross-lingual transferability to perform editing simultaneously across languages. |
| Approach: | They propose a framework to naturally adapt monolingual model editing approaches to the cross-lingual scenario using parallel corpus. |
| Outcome: | The proposed framework adapts monolingual model editing approaches to the cross-lingual scenario using parallel corpus and amplifies different subsets of parameters for each language. |
CINO: A Chinese Minority Pre-trained Language Model (2022.coling-1)
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| Challenge: | Existing multilingual pre-trained language models do not perform well on some low-resource languages. |
| Approach: | They propose a multilingual pre-trained language model for Chinese minority languages . they collect documents from Wikipedia and construct two classification datasets . |
| Outcome: | The proposed model outperforms baseline models on various classification tasks. |
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning (2022.coling-1)
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| Challenge: | Multilingual pre-trained language models have shown impressive performance on several downstream tasks for both high-resourced and low-resource languages. |
| Approach: | They propose to apply multilingual adaptive fine-tuning to 17 most-resourced African languages and three other high-resource languages to encourage cross-lingual transfer learning. |
| Outcome: | The proposed approach is competitive to LAFT on individual languages while requiring significantly less disk space. |
An Empirical Study of Pre-trained Transformers for Arabic Information Extraction (2020.emnlp-main)
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| Challenge: | Multilingual pre-trained Transformers have been shown to enable effective cross-lingual zero-shot transfer, but their performance on Arabic information extraction tasks is not well studied. |
| Approach: | They pre-train a bilingual BERT that is designed specifically for Arabic NLP and English-to-Arabic zero-shot transfer learning. |
| Outcome: | The pre-trained model significantly outperforms mBERT, XLM-RoBERTa, and AraBERT in both the supervised and zero-shot transfer settings. |
Soft Language Clustering for Multilingual Model Pre-training (2023.acl-long)
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| Challenge: | Multilingual pre-trained language models have demonstrated impressive (zero-shot) cross-lingual transfer abilities, however, their performance is hindered when the target language has distant typology from the source language or when pre-training data is limited in size. |
| Approach: | They propose a method that contextually retrieves prompts as flexible guidance for encoding instances conditionally. |
| Outcome: | The proposed method improves on the XTREME task and also for low-resource languages in unsupervised sentence retrieval. |
Discovering Language-neutral Sub-networks in Multilingual Language Models (2022.emnlp-main)
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| Challenge: | a recent study shows that multilingual pre-trained language models transfer well on cross-lingual downstream tasks. |
| Approach: | They conceptualize language neutrality as a function of overlap between language-encoding sub-networks of multilingual models. |
| Outcome: | The proposed model performs well on cross-lingual tasks despite being pre-trained on multiple languages . |
Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment (2024.findings-emnlp)
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| Challenge: | Recent mPLMs have shown impressive performance on crosslingual transfer tasks . however, the performance is often hindered when a lowresource target language is written in a different script than the high-resource source language. |
| Approach: | They propose a transliteration-based method to improve cross-lingual alignment between languages using diverse scripts. |
| Outcome: | The proposed method outperforms the original model on Englishcentric transfer tasks up to 50%. |
Efficient Unseen Language Adaptation for Multilingual Pre-Trained Language Models (2024.emnlp-main)
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| Challenge: | Multilingual pre-trained language models (mPLMs) have demonstrated notable effectiveness in zero-shot cross-lingual transfer tasks. |
| Approach: | They propose a method that uses soft-prompt tuning to tune for language adaptation . prompt tuning outperforms continuously trained baselines on two benchmarks . |
| Outcome: | The proposed approach outperforms baselines on two text classification benchmarks while utilizing 0.28% of tuned parameters. |