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Efficient Monotonic Multihead Attention
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 offli...
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Published in: | arXiv.org 2023-12 |
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creator | Ma, Xutai Sun, Anna Ouyang, Siqi Inaguma, Hirofumi Paden Tomasello |
description | 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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subjects | Alignment |
title | Efficient Monotonic Multihead Attention |
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