论文标题
mulzdg：零击对话生成的多语言代码切换框架
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
论文作者
论文摘要
在零拍摄的情况下建立对话的生成系统仍然是一个巨大的挑战，因为对话生成中典型的零击方法在很大程度上取决于大规模的预训练的语言生成模型，例如GPT-3和T5。由于缺乏相应的平行对话COLIDA，有关无繁琐语言模型的零摄像对话生成的研究受到限制。在本文中，我们提出了一个简单但有效的多语言学习框架，用于零拍对对话（称为Mulzdg），该框架可以有效地将知识从带有大规模培训样本的英语语料库转移到具有零样本的非英语语料库。此外，MulzDG可以被视为一种多语言数据增强方法，以提高资源丰富的语言的性能。首先，我们通过从单语英文数据集随机选择的翻译说法构建多语言代码转换对话数据集。然后，我们使用MulzDG根据代码转换数据集训练统一的多语言对话模型。 mulzdg可以在不同语言之间进行隐式语义一致性。关于DailyDialog和DSTC7数据集的实验表明，与培训相比，MulzDG不仅在零击中的情况下实现了竞争性能，而且还可以大大提高源语言的性能。
Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5. The research on zero-shot dialogue generation without cumbersome language models is limited due to lacking corresponding parallel dialogue corpora. In this paper, we propose a simple but effective Multilingual learning framework for Zero-shot Dialogue Generation (dubbed as MulZDG) that can effectively transfer knowledge from an English corpus with large-scale training samples to a non-English corpus with zero samples. Besides, MulZDG can be viewed as a multilingual data augmentation method to improve the performance of the resource-rich language. First, we construct multilingual code-switching dialogue datasets via translation utterances randomly selected from monolingual English datasets. Then we employ MulZDG to train a unified multilingual dialogue model based on the code-switching datasets. The MulZDG can conduct implicit semantic alignment between different languages. Experiments on DailyDialog and DSTC7 datasets demonstrate that MulZDG not only achieve competitive performance under zero-shot case compared to training with sufficient examples but also greatly improve the performance of the source language.
