多语言机器人深度学习模型构建

Construction of Deep Learning Model for Multilingual Robot

为了将中英文对话机器人已有的神经语言程序(NLP)能力拓展到更多语言,满足混合语言人机交互场景需求,分析了新语言特性预处理机制,提出了一种多语言机器人深度学习模型.通过多任务联合训练翻译模型构建、引入判别器对抗训练、词向量语料共享、本地化挖掘映射向量空间、跨语言知识蒸馏技术等创新方法,实现了不同语言环境下的知识迁移和自动迭代.实验结果表明,跨语言模型在单语测试和混合语言测试上均达到了预期结果,证明了该模型的有效性.

To expend the existing neuro-lingusitic programming capabilities of Chinese-English dialog robots to more languages and meet the needs of human-computer interaction scenarios in mixed languages,we analyze the preprocessing mechanism of new language characteristics and propose a multi-language robots model of deep learning. We construct a translation model through multi-task joint training,introduce discriminator antagonism training and word orientation. Innovative methods such as word vector corpus sharing,localized mining mapping vector space,and cross-language knowledge distillation technology have realized knowledge transfer and automatic iteration in different language environments. The results show that the cross-language model achieves the expected results in both monolingual and mixed-language tests,which proves the validity of the model.