论文标题
同时机器翻译的高效等待K型号
Efficient Wait-k Models for Simultaneous Machine Translation
论文作者
论文摘要
同时的机器翻译包括在整个输入序列可用之前的启动输出生成。 Wait-K解码器为此问题提供了一种简单但有效的方法。他们首先读取K源代币，然后在产生目标令牌和阅读另一个源代币之间进行交替。我们使用IWSLT数据集研究了在低资源设置中对Wait-K解码的行为。我们使用单向编码器改善了这些模型的训练，并跨K的多个值训练。使用变压器和2D横向横向架构的实验表明，我们的Wait-K模型在广泛的延迟级别上很好地推广。我们还表明，二维卷积体系结构与变压器具有同时翻译语言的竞争力。
Simultaneous machine translation consists in starting output generation before the entire input sequence is available. Wait-k decoders offer a simple but efficient approach for this problem. They first read k source tokens, after which they alternate between producing a target token and reading another source token. We investigate the behavior of wait-k decoding in low resource settings for spoken corpora using IWSLT datasets. We improve training of these models using unidirectional encoders, and training across multiple values of k. Experiments with Transformer and 2D-convolutional architectures show that our wait-k models generalize well across a wide range of latency levels. We also show that the 2D-convolution architecture is competitive with Transformers for simultaneous translation of spoken language.
