Papers with AmericasNLI
DeFT-X: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Prior studies have explored multiple approaches to combine task knowledge from task-specific data in a (high-resource) source language with language knowledge from unlabeled text in 'low-resourced' target language. |
| Approach: | They propose a composable sparse fine-tuning approach that learns task-specific and language-specific sparsen masks to select a subset of the pretrained model's parameters. |
| Outcome: | The proposed approach performs at par or outperforms SFT and other prominent cross-lingual transfer baselines. |
Composable Sparse Fine-Tuning for Cross-Lingual Transfer (2022.acl-long)
Copied to clipboard
| Challenge: | Adapters and sparse fine-tuning have been developed to improve transfer learning . a number of approaches have been proposed to improve performance of fine-untuners . |
| Approach: | They propose a method that fine-tunes the entire set of parameters of a large pretrained model . they use adapters and sparse fine-uning to improve model efficiency . |
| Outcome: | The proposed method outperforms adapters in cross-lingual transfer benchmarks. |
Unifying Cross-Lingual Transfer across Scenarios of Resource Scarcity (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to deal with resource scarcity have not been developed to deal effectively with the problem. |
| Approach: | They propose to use a set of tools to harness data from one or more high-resource "source" languages to compensate for a shortage of data in low-resourced "target" languages. |
| Outcome: | The proposed technique can be easily adapted to unseen languages, extending the range of the proposed technique and translation-based transfer more broadly. |
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages (2022.acl-long)
Copied to clipboard
Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Vladimir Meza Ruiz, Gustavo Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Thang Vu, Katharina Kann
| Challenge: | Pretrained multilingual models can perform cross-lingual transfer in a zero-shot setting, even for unseen languages. |
| Approach: | They propose to extend XNLI to 10 indigenous languages of the Americas and test multiple zero-shot and translation-based approaches. |
| Outcome: | The proposed model can perform cross-lingual transfer in a zero-shot setting even for languages unseen during pretraining. |