论文标题
筛查神经机器翻译中的性别转移
Screening Gender Transfer in Neural Machine Translation
论文作者
论文摘要
本文旨在确定最先进的机器翻译系统中的信息流，例如从法语翻译成英语时性别的转移。使用一组受控的示例，我们实验了几种方法来研究性别信息如何在编码器编码器体系结构中循环，以考虑探测技术以及对MT系统中使用的内部表示形式的干预措施。我们的结果表明，在由编码器和解码器构建的所有令牌表示中可以找到性别信息，并导致我们得出结论，性别转移有多种途径。
This paper aims at identifying the information flow in state-of-the-art machine translation systems, taking as example the transfer of gender when translating from French into English. Using a controlled set of examples, we experiment several ways to investigate how gender information circulates in a encoder-decoder architecture considering both probing techniques as well as interventions on the internal representations used in the MT system. Our results show that gender information can be found in all token representations built by the encoder and the decoder and lead us to conclude that there are multiple pathways for gender transfer.
