基于多策略原型生成的低资源神经机器翻译

资源丰富场景下,利用相似性翻译作为目标端原型序列,能够有效提升神经机器翻译的性能.然而在低资源场景下,由于平行语料资源匮乏,导致不能匹配得到原型序列或序列质量不佳.针对此问题,提出一种基于多种策略进行原型生成的方法.首先结合利用关键词匹配和分布式表示匹配检索原型序列,如未能获得匹配,则利用伪原型生成方法产生可用的伪原型序列.其次,为有效地利用原型序列,对传统的编码器-解码器框架进行改进.编码端使用额外的编码器接收原型序列输入;解码端在利用门控机制控制信息流动的同时,使用改进的损失函数减少低质量原型序列对模型的影响.多个数据集上的实验结果表明,相比基线模型,所提出的方法能够有效提升低资源场景下的机器翻译性能.

Low-resource Neural Machine Translation with Multi-strategy Prototype Generation

In rich-resource scenarios, using similarity translation as the target prototype sequence can improve the performance of neural machine translation. However, in low-resource scenarios, due to the lack of parallel corpus resources, the prototype sequence cannot be matched, or the sequence quality is poor. To address this problem, this study proposes a low-resource neural machine translation approach with multi-strategy prototype generation, and the approach includes two phases. (1) Keyword matching and distributed representation matching are combined to retrieve prototype sequences, and the pseudo prototype generation approach is leveraged to generate available prototype sequences during retrieval failures. (2) The conventional encoder-decoder framework is improved for the effective employment of prototype sequences. The encoder side utilizes additional encoders to receive prototype sequences. The decoder side, while employing a gating mechanism to control information flow, adopts improved loss functions to reduce the negative impact of low-quality prototype sequences on the model. The experimental results on multiple datasets show that the proposed method can effectively improve the translation performance compared with the baseline models.