Papers with monolingual model
Language Clustering for Multilingual Named Entity Recognition (2021.findings-emnlp)
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| Challenge: | Recent work in multilingual natural language processing has shown progress on tasks such as natural language inference and joint multilingual translation. |
| Approach: | They propose a technique that groups similar languages together by embeddings from a pre-trained masked language model and automatically discovering language clusters in this embeddable space. |
| Outcome: | The proposed technique outperforms baselines on 15 languages in the WikiAnn dataset showing meaningful multilingual transfer for low-resource languages (Swahili and Yoruba). |
Entity Projection via Machine Translation for Cross-Lingual NER (D19-1)
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| Challenge: | a subset of languages have large annotated corpora for named entity recognition. |
| Approach: | They propose a system that leverages machine translation systems twice to improve named entity recognition. |
| Outcome: | The proposed system outperforms existing methods on Armenian languages by 4.1 points . it achieves state-of-the-art F_1 scores for Armenian, outperforming monolingual model trained on Armenia. |
Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank (2020.findings-emnlp)
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| Challenge: | Pretrained multilingual contextual representations have shown great success, but their benefits do not apply equally to all language varieties. |
| Approach: | They propose to use additional language-specific pretraining and vocabulary augmentation to adapt multilingual models to low-resource settings. |
| Outcome: | The proposed methods significantly improve performance over baselines, especially in the lowest-resource cases, and demonstrate the importance of the relationship between such models’ pretraining data and target language varieties. |
Adaptation of Deep Bidirectional Transformers for Afrikaans Language (2020.lrec-1)
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| Challenge: | Pretrained language models can be trained in unsupervised manner, but can be difficult to implement because of the amount of data and computational resources needed for pretraining. |
| Approach: | They propose a model for Afrikaans based on bidirectional encoder representation from transformers. |
| Outcome: | The proposed model outperforms the existing models in part-of-speech tagging, named-entity recognition, and dependency parsing tasks. |
Data-Efficient French Language Modeling with CamemBERTa (2023.findings-acl)
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| Challenge: | Recent advances in NLP have significantly improved the performance of language models on a variety of tasks. |
| Approach: | They introduce a French DeBERTa model that builds upon the DeBERTAV3 architecture and training objective and evaluate its performance on a variety of French downstream tasks and datasets. |
| Outcome: | The proposed model outperforms BERT-based models on most tasks given the same amount of training tokens and trained on 30% of its input tokens. |
Revisiting Machine Translation for Cross-lingual Classification (2023.emnlp-main)
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| Challenge: | Recent work in cross-lingual learning has pivoted around multilingual models, which are typically pretrained on unlabeled corpora in multiple languages using some form of language modeling objective. |
| Approach: | They propose to use a stronger machine translation system to mitigat mismatch between training on original text and running inference on machine translated text. |
| Outcome: | The proposed approach is highly task dependent and calls into question the dominance of multilingual models for cross-lingual classification. |
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. |
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
| Challenge: | Multilingual language models have been a crucial breakthrough for under-resourced languages . however, the superiority of language-specific models has already been proven for underresourced ones . |
| Approach: | They propose to build a monolingual monolingual model that is comparable to state-of-the-art large multilingual models. |
| Outcome: | The proposed model consistently outperforms state-of-the-art models across tasks and settings. |
Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual Applications (2024.lrec-main)
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| Challenge: | Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference data to build high-performance models can outperform conventional methods. |
| Approach: | They propose a multilingual sentence embedding model by extending an existing monolingual model by using the low-rank adaptation technique. |
| Outcome: | The proposed model outperforms the previous approach and shows that languages with fewer resources or those with less linguistic similarity to English benefit more from the parameter increase. |
Leveraging Multilingual Training for Authorship Representation: Enhancing Generalization across Languages and Domains (2025.emnlp-main)
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| Challenge: | Authorship representation (AR) models capture an author's distinctive writing style by encoding documents written by the same author as nearby vectors in the embedding space. |
| Approach: | They propose a method that incorporates probabilistic content masking and language-aware batching to improve contrastive learning by reducing cross-lingual interference. |
| Outcome: | The proposed model outperforms monolingual baselines in 21 out of 22 non-English languages and reaches a maximum gain of 15.91% in a single language. |