Enhancing Code-Switching for Cross-lingual SLU: A Unified View of Semantic and Grammatical Coherence
Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Dongsheng Chen, Yuexian Zou · 2023
Despite the success of spoken language understanding (SLU) in high-resource languages, achieving similar performance in low-resource settings, such as zero-shot scenarios, remains challenging due to limited labeled training data.To improve zero-shot cross-lingual SLU, recent studies have explored code-switched sentences containing tokens from multiple languages.However, vanilla code-switched sentences often lack semantic and grammatical coherence.We ascribe this lack to two issues: (1) randomly replacing code-switched tokens with equal probability and (2) disregarding token-level dependency within each language.To tackle these issues, in this paper, we propose a novel method termed SOGO, for zero-shot cross-lingual SLU.First, we use a saliency-based substitution approach to extract keywords as substitution options.Then, we introduce a novel token-level alignment strategy that considers the similarity between the context and the code-switched tokens, ensuring grammatical coherence in code-switched sentences.Extensive experiments and analyses demonstrate the superior performance of SOGO across nine languages on MultiATIS++.