Key points are not available for this paper at this time.
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.
Building similarity graph...
Analyzing shared references across papers
Loading...
Liu et al. (Fri,) studied this question.
www.synapsesocial.com/papers/68e5c0e5b6db6435875585d2 — DOI: https://doi.org/10.48550/arxiv.2408.08724
Yongkang Liu
Shi Feng
Daling Wang
Building similarity graph...
Analyzing shared references across papers
Loading...
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: