Papers › End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic...
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning
Nikhil Kumar Lakumarapu, Beomseok Lee, Sathish Reddy Indurthi, Hou Jeung Han, Mohd Abbas Zaidi, Sangha Kim
In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task. We adopt the Transformer architecture coupled with the meta-learning approach to build our end-to-end Speech-to-Text Translation (ST) system. Our meta-learning approach tackles the data scarcity of the ST task by leveraging the data available from Automatic Speech Recognition (ASR) and Machine Translation (MT) tasks. The meta-learning approach combined with synthetic data augmentation techniques improves the model performance significantly and achieves BLEU scores of 24.58, 27.51, and 27.61 on IWSLT test 2015, MuST-C test, and Europarl-ST test sets respectively.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech-to-Text Translation | MuST-C EN->DE | Transformer + Meta Learning(ASR/MT) + Data Augmentation | Case-sensitive sacreBLEU | 27.51 | #3 of 8 | Archive leaderboard | report |
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