| 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. |
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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. |
| Outcome: | The proposed methods outperform vanilla multilingual fine-tuning on two cross-lingual classification benchmarks. |
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT (2021.eacl-main)
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| Challenge: | Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. |
| Approach: | They propose to use a layer ablation technique to create a multilingual model that is viewed as a stacking of two sub-networks: a language-agnostic encoder and a task-specific predictor. |
| Outcome: | The proposed model can perform zero-shot cross-lingual transfer for many languages. |
Identifying Elements Essential for BERT’s Multilinguality (2020.emnlp-main)
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| Challenge: | Multilingual BERT (mBERT) does not use any crosslingual signal during training. |
| Approach: | They propose a multilingual pretraining setup that modifies the masking strategy using VecMap to allow for fast experimentation. |
| Outcome: | The proposed setup with pretrained models with three languages shows that it works well. |
Extending Multilingual BERT to Low-Resource Languages (2020.findings-emnlp)
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| Challenge: | Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. |
| Approach: | They propose a simple but effective approach to extend multilingual BERT to any new language and show an increase in F1 on M-BERT and new languages. |
| Outcome: | The proposed approach improves on languages already in M-BERT and out of it on other languages. |
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 . |
| Approach: | They propose an approach that transfers a monolingual model to new languages at the lexical level. |
| Outcome: | The proposed approach is competitive with multilingual BERT on cross-lingual classification benchmarks and on a new cross-linguistic question answering dataset. |
When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer (2022.naacl-main)
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| Challenge: | Recent work on multilingual language models has demonstrated their capacity for cross-lingual zero-shot transfer on downstream tasks. |
| Approach: | They conduct a large-scale empirical study to isolate the effects of various linguistic properties by measuring zero-shot transfer between four different natural languages. |
| Outcome: | The proposed model exhibits decent cross-lingual zero-shot transfer, with no significant differences in word order and embedding alignment. |
Finding Universal Grammatical Relations in Multilingual BERT (2020.acl-main)
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| Challenge: | Recent work has found that multilingual masked language models learn a surprising amount of linguistic structure, despite a lack of direct linguistic supervision. |
| Approach: | They propose an unsupervised method to find syntactic tree distances in languages other than English and that these subspaces are approximately shared across languages. |
| Outcome: | The proposed method shows that mBERT learns representations of syntactic dependency labels, in the form of clusters, which largely agree with the Universal Dependencies taxonomy. |
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT (D19-1)
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| Challenge: | Pretrained contextual representation models have pushed forward the state-of-the-art on many NLP tasks. |
| Approach: | They propose to use a model that is pretrained on 104 languages for cross-lingual transfer. |
| Outcome: | The proposed model performs well on 5 NLP tasks covering 39 languages from various language families. |
BERTGen: Multi-task Generation through BERT (2021.acl-long)
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| Challenge: | Recent work in unsupervised and self-supervised pre-training has revolutionised the field of natural language understanding (NLU). |
| Approach: | They propose to use multimodal and multilingual pre-trained models to extend BERT by fusing them together for language generation tasks. |
| Outcome: | The proposed model outperforms baseline models in image captioning, machine translation and multimodal machine translation tasks and is competitive with supervised counterparts. |
Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations (D19-61)
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| Challenge: | Pretrained sentence representations have set the new state of the art in many language understanding tasks. |
| Approach: | They propose to use a multilingual corpus to train deep bidirectional sentence representations that are fully lexicalized to allow for the development of an unsupervised universal dependency parser. |
| Outcome: | The proposed approach outperforms the best CoNLL 2018 systems in all of the shared task’s six truly low-resource languages while using a single system. |