| Challenge: | Recent advances in pretrained contextual representation models have made significant progress on a number of different English NLP tasks. |
| Approach: | They propose a robust framework to include unlabeled non-English samples in the fine-tuning process of pretrained multilingual representation models. |
| Outcome: | The proposed framework includes unlabeled non-English samples in the fine-tuning process of pretrained multilingual representation models. |
Similar Papers
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent pre-trained language models have achieved remarkable zero-shot performance . we propose a self-learning framework that utilizes unlabeled data of target languages . |
| Approach: | They propose a self-learning framework that utilizes unlabeled data of target languages to select silver labels for cross-lingual transfer tasks. |
| Outcome: | The proposed framework outperforms baseline models on two cross-lingual tasks by 10 F1 on average and 2.5 accuracy on natural language inference (NLI). |
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach (2021.naacl-main)
Copied to clipboard
| Challenge: | Fine-tuned pre-trained language models (LMs) have enormous success in many natural language processing tasks, but they still require excessive labeled data in the fine-tuning stage. |
| Approach: | They propose a framework to enable fine-tuning pre-trained language models with weak supervision without any labeled data. |
| Outcome: | The proposed framework outperforms the strongest baseline and achieves competitive performance with fully-supervised fine-tuning methods. |
Effective Fine-Tuning Methods for Cross-lingual Adaptation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Large scale multilingual pre-trained language models have shown promising results in zero- and few-shot cross-lingual tasks. |
| Approach: | They propose a co-tuning method that aims to learn more generalized semantic equivalences when the languages are structurally dissimilar. |
| Outcome: | The proposed method improves on cross-lingual inference and review tasks by capturing the semantic relationship in the parallel data when a few translation pairs are available. |
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. |
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for fully unsupervised cross-lingual mapping of word embeddings are available to achieve such a mapping . |
| Approach: | They reproduce the experiments of Artetxe and Sgaard (2018) . they propose a robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. |
| Outcome: | The proposed method is feasible with minor assumptions, and it is able to be replicated in four languages. |
Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Pretrained Language Models (PLMs) are advanced but data labels are noisy due to the complex annotation process. |
| Approach: | They propose a framework for fine-tuning PLMs using noisy labels that incorporates guidance from Large Language Models like ChatGPT. |
| Outcome: | Experiments on synthetic and real-world noisy datasets show that the proposed framework outperforms the state-of-the-art framework. |
Multilingual and cross-lingual document classification: A meta-learning approach (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods to document classification in low-resource languages are under-resourced . 6% of the world's languages are spoken, and many have inadequate resources . |
| Approach: | They propose a meta-learning approach to document classification in low-resource languages . they propose 'nuclear-shot' cross-lingual adaptation to previously unseen languages based on limited data . |
| Outcome: | The proposed method performs on-par on some languages while under-resourced in others. |
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification (2021.eacl-main)
Copied to clipboard
| Challenge: | Semi-supervised learning and multilingual pretraining have been shown to be effective for task-specific labelled data shortages. |
| Approach: | They propose to combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. |
| Outcome: | The proposed method outperforms state-of-the-art models in low-resource settings across several languages and outperformed existing models in English. |
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings (P18-1)
Copied to clipboard
| Challenge: | Existing methods to learn cross-lingual word embeddings have failed in more realistic scenarios . a fully unsupervised initialization and a robust self-learning algorithm are needed to improve the existing methods. |
| Approach: | They propose an unsupervised initialization method that exploits structural similarity of embeddings and a robust self-learning algorithm that iteratively improves it. |
| Outcome: | The proposed method achieves the best published results in standard datasets even surpassing previous supervised systems. |
Language Contamination Helps Explains the Cross-lingual Capabilities of English Pretrained Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | a large number of pretraining corpora are not publicly available, and it is unclear how much foreign language data exists in monolingual models. |
| Approach: | They propose to use English pretraining corpora to analyze their language composition . they find that even when less than 1% of data is not English, it facilitates cross-lingual transfer . |
| Outcome: | The proposed model is not truly monolingual when pretrained at scale, the authors show . they show that even when less than 1% of data is not English, it facilitates cross-lingual transfer . |