Papers by Asa Cooper Stickland
Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining (2023.eacl-main)
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| Challenge: | Existing studies on robustness of pretrained multilingual models are limited to the English language. |
| Approach: | They propose to use data augmentation and contrastive loss term to boost robustness of multilingual models in cross-lingual settings. |
| Outcome: | The proposed model outperforms existing models on clean and noisy data in the cross-lingual setting. |
When does Parameter-Efficient Transfer Learning Work for Machine Translation? (2022.emnlp-main)
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| Challenge: | Prior work indicates that parametric fine-tuning methods may not work as well for machine translation (MT). |
| Approach: | They propose to use parameter-efficient fine-tuning methods to adapt large pre-trained models while only tuning a small number of parameters. |
| Outcome: | The proposed methods outperform full fine-tuning for many downstream tasks when the parameter budget corresponds to 10% of the model parameters. |
Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation (2021.eacl-main)
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| Challenge: | Recent advances in machine translation (MT) have improved performance on low-resource language pairs. |
| Approach: | They propose to freeze most BART parameters and add new ones to fine-tune a model trained on MT. |
| Outcome: | The proposed model outperforms naive fine-tuning on Vietnamese to English on a training set for Vietnamese to Vietnamese . the proposed model is able to fine- tune on smaller datasets while still maintaining the same model performance. |