Papers with M-BERT
A Multilingual Reading Comprehension System for more than 100 Languages (2020.coling-demos)
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Anthony Ferritto, Sara Rosenthal, Mihaela Bornea, Kazi Hasan, Rishav Chakravarti, Salim Roukos, Radu Florian, Avi Sil
| Challenge: | Recent advances in open domain question answering (QA) have focused on machine reading comprehension (MRC) |
| Approach: | They propose a multilingual machine reading comprehension (MRC) demo which can answer questions in over 100 languages. |
| Outcome: | The proposed system can answer questions in over 100 languages and integrates with IBM Watson's machine translation widget to improve language accessibility. |
Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers (2020.coling-industry)
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| Challenge: | scalable Universal Dependency (UD) treebank synthesis techniques are used to improve production-grade parsers. |
| Approach: | They propose a data augmentation technique that uses synthetic treebanks to improve production-grade parsers. |
| Outcome: | The proposed technique improves LAS performance on seven languages by up to two points on production models trained on original UD treebanks. |
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. |
X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering (2021.naacl-main)
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| Challenge: | Multilingual models have gained popularity for their zero-shot cross-lingual transfer learning capabilities, but their generalization ability is inconsistent for typologically diverse languages. |
| Approach: | They propose a meta-learning approach that adapts MAML to learn to adapt to new languages . they extensively evaluate two cross-lingual NLU tasks using English as source and spanish as target . |
| Outcome: | The proposed approach outperforms naive fine-tuning on cross-lingual tasks for most languages. |
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. |
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. |
mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences (2023.findings-emnlp)
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| Challenge: | a new text-to-text transformer is suitable for multilingual inputs . many of the current models are English-only, making them inapplicable to other languages. |
| Approach: | They propose to extend a multilingual text-to-text transformer to handle long inputs . they use the mC4 dataset to pretrain the model to handle multilingual data . |
| Outcome: | The proposed model performs well on multilingual summarization and question-answering tasks. |