论文标题
角色级翻译自我注意
Character-Level Translation with Self-attention
论文作者
论文摘要
我们探讨了自我注意力模型对角色级神经机器翻译的适用性。我们测试了标准变压器模型，以及一种新颖的变体，其中编码器块使用卷积结合了附近字符的信息。我们在WMT和联合国数据集上进行了广泛的实验，对使用多达三种输入语言（法语，西班牙语和中文）进行双语和多语言翻译测试。我们的变压器变体始终优于字符级别的标准变压器，并更快地收敛，同时学习更强大的字符级比对。
We explore the suitability of self-attention models for character-level neural machine translation. We test the standard transformer model, as well as a novel variant in which the encoder block combines information from nearby characters using convolutions. We perform extensive experiments on WMT and UN datasets, testing both bilingual and multilingual translation to English using up to three input languages (French, Spanish, and Chinese). Our transformer variant consistently outperforms the standard transformer at the character-level and converges faster while learning more robust character-level alignments.
