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.
翻译:同时的机器翻译包括在整个输入序列提供之前开始产出生成。 Wait-k 解码器为这一问题提供了一个简单但有效的方法。 他们首先读取 k 源符号, 之后他们轮流在生产目标符号和读取另一个源符号之间进行交替。 我们使用 IWSLT 数据集调查低资源设置中口语公司在低资源设置中待到的解码行为。 我们用单向编码器改进对这些模型的培训,并且通过 k. 变换器和 2D 革命结构的多重值培训, 表明我们的等待- k 模型在广泛的延时级别上非常普及。 我们还显示, 2D 变换器在同时翻译口语方面具有竞争力 。