LaDA：用于零样本跨语言神经网络语言建模的潜在对话动作
LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling
AI总结：
本文提出潜在对话动作层LaDA，通过在解码阶段引入潜在变量优化跨语言口语理解策略，显著提升远距离语言在零样本和少样本自适应下的意图检测与槽位填充性能。
摘要：
跨语言自适应已被证明在资源有限的口语理解（SLU）系统中是有效的。现有方法在意图检测和槽位填充方面常常不能令人满意，特别是对于那些在文字系统、形态学和句法上与源语言有显著差异的远距离语言。为了解决上述问题，提出了潜在对话动作（LaDA）层来优化解码策略。该模型由一个额外的潜在对话动作层组成。它使我们的模型能够提升系统处理具有复杂多语言意图和远距离语言槽位值的对话的能力。据我们所知，这是首次对在解码阶段使用潜在变量来优化跨语言SLU策略进行的详尽研究。LaDA在零样本和少样本自适应的公开数据集上均取得了最先进的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
