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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
%A Liu, Yongkang
%A Feng, Shi
%A Wang, Daling
%A Zhang, Yifei
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F liu-etal-2022-mulzdg
%X 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.
%U https://aclanthology.org/2022.coling-1.54
%P 648-659

Markdown (Informal)

[MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation](https://aclanthology.org/2022.coling-1.54) (Liu et al., COLING 2022)

• MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation (Liu et al., COLING 2022)

ACL

• Yongkang Liu, Shi Feng, Daling Wang, and Yifei Zhang. 2022. MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 648–659, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.