Assessing the Syntactic Capabilities of Transformer-based Multilingual Language Models (2021.findings-acl)
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| Challenge: | Multilingual Transformer-based language models have been shown to be excellent learners in crosslingual transfer tasks. |
| Approach: | They evaluate the syntactic generalization capabilities of BERT and RoBERTa models on English and Spanish tests. |
| Outcome: | The proposed models perform well on English and Spanish tests, and the proposed tests are compared against models on the same language and models on two different languages. |
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| Challenge: | Generative Pre-trained Transformers (GPTs) have been scaled to unprecedented sizes in the history of machine learning. |
| Approach: | They investigate the potential and limits of Generative Pre-trained Transformers in three tasks . they find it can be almost as useful for many languages as it is for English . |
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On the Cross-lingual Transferability of Monolingual Representations (2020.acl-main)
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| Challenge: | State-of-the-art unsupervised multilingual models generalize in zero-shot cross-lingual setting . generalization ability attributed to shared subword vocabulary and joint training across multiple languages . |
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Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan (2021.findings-acl)
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Jordi Armengol-Estapé, Casimiro Pio Carrino, Carlos Rodriguez-Penagos, Ona de Gibert Bonet, Carme Armentano-Oller, Aitor Gonzalez-Agirre, Maite Melero, Marta Villegas
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Cross-lingual Transfer of Monolingual Models (2022.lrec-1)
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| Challenge: | Existing studies on cross-lingual learning using multilingual models cast doubt on shared vocabulary and joint pre-training . et al. (2005) show that model knowledge learned in the source language enhances the learning of the target language independently of language proximity. |
| Approach: | They propose a method for transferring monolingual models to other languages through continuous pre-training and investigate their results in English. |
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Practical Transformer-based Multilingual Text Classification (2021.naacl-industry)
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| Challenge: | XNLI does not reflect the data availability and task variety of industry applications. |
| Approach: | They compare transformer-based text classification methods to multilingual models in five different languages . they use a task- and domain-adaptive pretraining and data augmentation technique . |
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Unsupervised Cross-lingual Representation Learning at Scale (2020.acl-main)
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Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov
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Are Structural Concepts Universal in Transformer Language Models? Towards Interpretable Cross-Lingual Generalization (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have implicitly transfer knowledge across languages, but not all languages have such generalization capabilities. |
| Approach: | They propose a meta-learning-based method to learn to align conceptual spaces of different languages to enhance cross-lingual generalization. |
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GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models (2025.emnlp-demos)
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Hengyu Luo, Zihao Li, Joseph Attieh, Sawal Devkota, Ona de Gibert, Xu Huang, Shaoxiong Ji, Peiqin Lin, Bhavani Sai Praneeth Varma Mantina, Ananda Sreenidhi, Raúl Vázquez, Mengjie Wang, Samea Yusofi, Fei Yuan, Jörg Tiedemann
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| Outcome: | The proposed framework integrates 27 benchmarks under a standard ISO 639-3 language identifier system, allowing for seamless incorporation of new benchmarks. |
Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)
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| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
| Approach: | They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems. |
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How Multilingual is Multilingual BERT? (P19-1)
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| Challenge: | Existing studies have shown that deep, contextualized language models can encode syntactic and named entity information, but they have focused on what models trained on English capture about English. |
| Approach: | They propose a multilingual model pre-trained from monolingual Wikipedia corpora . they show that multilingual BERT is surprisingly good at zero-shot cross-lingual model transfer . |
| Outcome: | The proposed model can find translation pairs, but it exhibits systematic deficiencies affecting certain language pairs. |