Cross-lingual adaptation has proven effective in spoken language understanding (SLU) systems with limited resources. Existing methods are frequently unsatisfactory for intent detection and slot filling, particularly for distant languages that differ significantly from the source language in scripts, morphology, and syntax. Latent Dialogue Action (LaDA) layer is proposed to optimize decoding strategy in order to address the aforementioned issues. The model consists of an additional layer of latent dialogue action. It enables our model to improve a system's capability of handling conversations with complex multilingual intent and slot values of distant languages. To the best of our knowledge, this is the first exhaustive investigation of the use of latent variables for optimizing cross-lingual SLU policy during the decode stage. LaDA obtains state-of-the-art results on public datasets for both zero-shot and few-shot adaptation.

翻译：跨语言自适应已被证明在资源有限的口语理解（SLU）系统中有效。现有方法在意图检测和槽位填充任务中往往表现不佳，尤其是在处理与源语言在文字、形态和句法上存在显著差异的远距离语言时。本文提出潜在对话动作（LaDA）层以优化解码策略，从而解决上述问题。该模型包含一个额外的潜在对话动作层，能够提升系统处理包含复杂多语言意图及远距离语言槽值的对话能力。据我们所知，这是首次系统性地研究在解码阶段利用潜在变量优化跨语言SLU策略。LaDA在零样本和少样本自适应场景下的公开数据集上均取得了当前最优的结果。