End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning
HouJeung Han, Mohd Abbas Zaidi, Sathish Reddy Indurthi, Nikhil Kumar Lakumarapu, Beomseok Lee, Sang‐Ha Kim · 2020
In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge.The systems are built by adding wait-k and meta-learning approaches to the Transformer architecture.The systems are evaluated on different latency regimes.The simultaneous text-to-text translation achieved a BLEU score of 26.38 compared to the competition baseline score of 14.17 on the low latency regime (Average latency ≤ 3).The simultaneous speech-to-text system improves the BLEU score by 7.7 points over the competition baseline for the low latency regime (Average Latency ≤ 1000).