高效單調多頭注意力

摘要

我們介紹了高效單調多頭注意力（EMMA），這是一種最先進的同步翻譯模型，具有數值穩定且無偏的單調對齊估計。此外，我們提出了改進的訓練和推斷策略，包括從離線翻譯模型同時微調以及減少單調對齊變異。實驗結果表明，所提出的模型在西班牙語和英語翻譯任務的同步語音轉文本翻譯中達到了最先進的性能。

English

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.