Papers with multi-lingual models
Low-resource neural machine translation with morphological modeling (2024.findings-naacl)
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| Challenge: | Existing methods for character-based and sub-word tokenization are limited to the surface forms of the words. |
| Approach: | They propose a framework-solution for modeling complex morphology in low-resource settings using a transformer architecture and beam search-based decoder. |
| Outcome: | The proposed model improves translation performance on Kinyarwanda English translation using public-domain parallel text. |
Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks (2021.acl-long)
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| Challenge: | Prior research has found that only a few attention heads are important in each mono-lingual NLP task and pruning the remaining heads leads to comparable or improved performance of the model. |
| Approach: | They examine the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks. |
| Outcome: | The proposed model performs better with the remaining heads pruned than with the other models, the authors show . |
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition (2022.naacl-main)
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| Challenge: | Named entity recognition (NER) models trained on CoNLL do not transfer well to other domains, even within the same language. |
| Approach: | They propose a token-level gating layer to augment pre-trained multilingual transformers with gazetteers containing named entities (NE) from a target language or domain. |
| Outcome: | The proposed model improves on cross-lingual transfer with an F1 score of 92.92 for English and an average of 89.43 across all languages in CoNLL. |
Cross-lingual Continual Learning (2023.acl-long)
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| Challenge: | Existing multi-lingual representations such as the one-hop transfer learning pipeline are difficult to adapt to new languages. |
| Approach: | They propose a cross-lingual continuum learning paradigm that evaluates continuous learning approaches that adapt to emerging data from different languages. |
| Outcome: | The proposed model can be used to adapt to new languages in a sequential manner. |
RoBERT – A Romanian BERT Model (2020.coling-main)
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| Challenge: | Existing pre-trained language models learn contextualized representations by using unlabeled text data and obtain state of the art results on a multitude of NLP tasks. |
| Approach: | They propose a pre-trained BERT model for Romanian language processing and compare it with multi-lingual models on seven Romanian specific NLP tasks. |
| Outcome: | The proposed model outperforms multi-lingual models on seven Romanian specific NLP tasks on sentiment analysis, dialect and cross-dialect topic identification, and diacritics restoration. |