Papers with low-resource adaptation
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data (2021.acl-long)
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Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Naman Goyal, Francisco Guzmán, Pascale Fung, Philipp Koehn, Mona Diab
| Challenge: | linguistic overlap between low-resource languages and high-resourced languages is a major obstacle for training high-quality machine translation systems. |
| Approach: | They exploit linguistic overlap to facilitate translation to and from low-resource languages . they use monolingual data and parallel data in related high-resourced languages based on their method . |
| Outcome: | The proposed method significantly improves translation into low-resource language compared to baselines on 7 languages from three different language families. |
A Unified Speaker Adaptation Approach for ASR (2021.emnlp-main)
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| Challenge: | Adapting a model to target speakers requires a lot of compute and may cause catastrophic forgetting to the existing speakers. |
| Approach: | They propose a unified speaker adaptation approach consisting of feature adaptation and model adaptation. |
| Outcome: | The proposed model outperforms baseline models with 20.58% relative WER reduction and surpasses finetuning method by 2.54% on target speaker adaptation. |