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This paper describes the system submitted to the IWSLT 2021 MultilingualSpeech Translation (MultiST) task from Huawei Noah's Ark Lab. We use a unifiedtransformer architecture for our MultiST model, so that the data from differentmodalities (i.e., speech and text) and different tasks (i.e., SpeechRecognition, Machine Translation, and Speech Translation) can be exploited toenhance the model's ability. Specifically, speech and text inputs are firstlyfed to different feature extractors to extract acoustic and textual features,respectively. Then, these features are processed by a shared encoder--decoderarchitecture. We apply several training techniques to improve the performance,including multi-task learning, task-level curriculum learning, dataaugmentation, etc. Our final system achieves significantly better results thanbilingual baselines on supervised language pairs and yields reasonable resultson zero-shot language pairs.

Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 2021