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cs.CL2022★ 2 cited
BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric
Mingda Chen, Paul-Ambroise Duquenne, Pierre Andrews +4
End-to-End speech-to-speech translation (S2ST) is generally evaluated with text-based metrics. This means that generated speech has to be automatically transcribed, making the eval…
cs.CL2022
Leveraging Natural Supervision for Language Representation Learning and Generation
Mingda Chen
Recent breakthroughs in Natural Language Processing (NLP) have been driven by language models trained on a massive amount of plain text. While powerful, deriving supervision from t…
cs.CL2022★ 1 cited
Improving In-Context Few-Shot Learning via Self-Supervised Training
Mingda Chen, Jingfei Du, Ramakanth Pasunuru +4
Self-supervised pretraining has made few-shot learning possible for many NLP tasks. But the pretraining objectives are not typically adapted specifically for in-context few-shot le…