The state-of-the-art adaptive policies for Simultaneous Neural Machine Translation (SNMT) use monotonic attention to perform read/write decisions based on the partial source and target sequences.
Simultaneous neural machine translation(SNMT) models start emitting the target sequence before they have processed the source sequence.
In general, the direct Speech-to-text translation (ST) is jointly trained with Automatic Speech Recognition (ASR), and Machine Translation (MT) tasks.
Ranked #1 on Speech-to-Text Translation on MuST-C EN->DE (using extra training data)
Inspired by these learning patterns in humans, we suggest a simple yet generic task aware framework to incorporate into existing joint learning strategies.
The current re-translation approaches are based on autoregressive sequence generation models (ReTA), which generate tar-get tokens in the (partial) translation sequentially.
In this paper, we describe the system submitted to the IWSLT 2020 Offline Speech Translation Task.
Ranked #3 on Speech-to-Text Translation on MuST-C EN->DE (using extra training data)
In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge.
In the meta-learning phase, the parameters of the model are exposed to vast amounts of speech transcripts (e. g., English ASR) and text translations (e. g., English-German MT).