Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation (2021.eacl-main)
Copied to clipboard
| 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. |
Similar Papers
Multilingual Denoising Pre-training for Neural Machine Translation (2020.tacl-1)
Copied to clipboard
Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer
| Challenge: | Existing approaches to pre-train models focus on only English corpora, but this is not common in machine translation. |
| Approach: | They propose a sequence-to-sequence denoising auto-encoder pre-trained on monolingual corpora . they show that it produces significant performance gains across MT tasks . |
| Outcome: | The proposed model can achieve significant performance gains across a wide variety of MT tasks. |
Know Where You’re Going: Meta-Learning for Parameter-Efficient Fine-Tuning (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on parameter-efficient fine-tuning methods require additional measures after pre-training and before fine-uning. |
| Approach: | They propose to take parameter-efficient fine-tuning into consideration after pre-training and before fine-uning and use meta-learning to prime a model specifically for parameter-efficiency. |
| Outcome: | The proposed method improves on a pre-trained model with certain modifications and achieves 4.96 points on cross-lingual NER fine-tuning. |
Multilingual Translation from Denoising Pre-Training (2021.findings-acl)
Copied to clipboard
Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan
| Challenge: | Recent work shows potential of training one model for multilingual machine translation . but little has been explored on the potential to combine denoising pretraining with multilingual translation in a single model. |
| Approach: | They propose to combine denoising pretraining with multilingual machine translation in a single model. |
| Outcome: | The proposed model improves over models trained from scratch and bilingually for translation into English. |
Breaking Down Multilingual Machine Translation (2022.findings-acl)
Copied to clipboard
| Challenge: | Multilingual training is an essential ingredient in machine translation systems . but it has different effects in different multilingual settings, such as many-to-one, one-tomany and many- to-many learning . |
| Approach: | They compare multilingual training settings with encoders and decoders initialized by multilingual learning . they find important attention heads for each language pair and compare their correlations during inference . |
| Outcome: | The proposed models outperform the best models for high-resource languages and one-to-many models for low-resourced languages. |
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
| Approach: | They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages. |
| Outcome: | The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases. |
Evaluating Parameter-Efficient Finetuning Approaches for Pre-trained Models on the Financial Domain (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large-scale language models with millions, billions, or trillions of trainable parameters are becoming increasingly popular. |
| Approach: | They compare performance of financial BERT-like models to their fully fine-tuned counterparts by using parameter-efficient tuning methods. |
| Outcome: | The proposed approaches match full fine-tuning performance on common NLP tasks, but are less studied in finance. |
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (D19-1)
Copied to clipboard
| Challenge: | Pretrained language models require unlabelled data for training, while cross-lingual models underperform on low-resource languages. |
| Approach: | They propose a multi-lingual language model fine-tuning to train and fine- tune language models efficiently in their own language. |
| Outcome: | The proposed method outperforms existing models on two widely used datasets on cross-lingual classification tasks. |
Lightweight Adapter Tuning for Multilingual Speech Translation (2021.acl-short)
Copied to clipboard
| Challenge: | Adapter tuning is an efficient alternative to fine-tuning in NLP . a multilingual model could be outperformed by its bilingual counterparts . |
| Approach: | They propose to use adapter tuning to optimize for multilingual speech translation . they use pre-trained models to freeze pre-train parameters and inject lightweight modules . |
| Outcome: | The proposed adapters can specialize to specific language pairs with low extra cost . the proposed models outperform bilingual models on high-resource language pairs . |
Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide (2026.tacl-1)
Copied to clipboard
| Challenge: | Pre-trained language models provide strong foundations, but effective adaptation under data scarcity requires efficient and efficient fine-tuning techniques. |
| Approach: | They propose to review parameter-efficient fine-tuning techniques that lower training and deployment costs and domain and cross-lingual adaptation methods for both encoder and decoder models. |
| Outcome: | The proposed techniques lower training and deployment costs, domain and cross-lingual adaptation methods, and model specialization strategies. |
Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning (2022.coling-1)
Copied to clipboard
| Challenge: | Multilingual pre-trained language models have shown impressive performance on several downstream tasks for both high-resourced and low-resource languages. |
| Approach: | They propose to apply multilingual adaptive fine-tuning to 17 most-resourced African languages and three other high-resource languages to encourage cross-lingual transfer learning. |
| Outcome: | The proposed approach is competitive to LAFT on individual languages while requiring significantly less disk space. |