Papers with low-resource situations
Does Masked Language Model Pre-training with Artificial Data Improve Low-resource Neural Machine Translation? (2023.findings-eacl)
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| Challenge: | Pre-training masked language models with artificial data has been proven beneficial for several natural language processing tasks, however, it has been less explored for neural machine translation (NMT). |
| Approach: | They pre-trained masked language models with random sequences and created artificial data mimicking token frequency information from the real world. |
| Outcome: | The results show that pre-training models with artificial data improves translation performance in low-resource situations. |
Multi-stage Pre-training over Simplified Multimodal Pre-training Models (2021.acl-long)
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| Challenge: | Existing multimodal pre-training models require large amounts of training data and have huge model sizes, making them impossible to apply in low-resource situations. |
| Approach: | They propose a multi-stage pre-training method which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train a model in stages. |
| Outcome: | The proposed method outperforms the original model in Image-Text Retrieval task and outperformed the original LXMERT model in downstream tasks. |