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arXiv · 2408.08724

ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language

Yongkang Liu·Feng Shi·Daling Wang·Yifei Zhang·Hinrich Schütze
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Abstract

Although large language models(LLMs) show amazing capabilities, among various exciting applications discovered for LLMs fall short in other low-resource languages. Besides, most existing methods depend on large-scale dialogue corpora and thus building systems for dialogue generation in a zero-shot scenario remains a considerable challenge. To address this challenge, we propose a novel end-to-end zero-shot dialogue generation model ChatZero based on cross-lingual code-switching method. First, we construct code-switching language and pseudo-target language with placeholders. Then for cross-lingual semantic transfer, we employ unsupervised contrastive learning to minimize the semantics gap of the source language, code-switching language, and pseudo-target language that are mutually positive examples in the high dimensional semantic space. Experiments on the multilingual DailyDialog and DSTC7-AVSD datasets demonstrate that ChatZero can achieve more than 90\% of the original performance under the zero-shot case compared to supervised learning, and achieve state-of-the-art performance compared with other baselines.

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Yongkang Liu, Feng Shi, Daling Wang, Yifei Zhang, Hinrich Schütze. 2024-08-16. ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language. https://arxiv.org/abs/2408.08724

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