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Efficient Monotonic Multihead Attention
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We introduce the Efficient Monotonic Multihead Attention (EMMA), a state-of-the-art simultaneous translation model with numerically-stable and unbiased monotonic alignment estimation. In addition, we present improved training and inference strategies, including simultaneous fine-tuning from an offline translation model and reduction of monotonic alignment variance. The experimental results demonstrate that the proposed model attains state-of-the-art performance in simultaneous speech-to-text translation on the Spanish and English translation task.
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Cited by 1 Pith paper
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Overcoming Non-monotonicity in Transducer-based Streaming Generation
MonoAttn-Transducer trains Transducers with monotonic cross-attention estimated from forward-backward posterior alignments, improving simultaneous speech translation quality at similar latency.
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