End-to-End Simultaneous Translation System for IWSLT 2020 Using Modality Agnostic Meta-Learning (2020)

Abstract: 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).

## introduction simultaneous neural machine translation (snmt) addresses the problem of live interpretation in machine translation. in a traditional neural machine translation model, the encoder first reads the entire source sequence, and then the decoder generates the translated target sequence. on the other hand, a simultaneous neural machine translation model alternates between reading the source sequence and writing the target sequence using either a fixed or an adaptive policy. this would allow the model to avoid intolerable delay in live or streaming translation scenarios. in this work, we build a simultaneous translation system for text-to-text(t2t) and speech-to-text(s2t) problems based on transformer wait-k model @xcite . we adopt the meta-learning approach presented in @xcite to deal with the data scarcity issue in the speech-to-text translation task. the system architecture and data processing techniques are designed for the iwslt 2020 online translation task @xcite . however, these techniques can be applied to current and future snmt models as well. we conduct several experiments on both text-to-text and speech-to-text problems to evaluate the proposed system. our experimental results reveal that the proposed system achieves significant performance gains over the provided competition baselines on both the translation tasks. ## conclusion in this work, we develop an end-to-end simultaneous translation system for both text-to-text and speech-to-text tasks by using the wait-k method and the meta-learning approach. we evaluate the proposed system with different data settings and latency regimes. we explore the effectiveness of the meta-learning approach for the online translation tasks. the meta-learning approach proves to be essential in settings where the training data is scarce. compared to the baseline provided in the competition, both online text-to-text and speechto-text models achieved significant bleu score improvements.