LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network
  Language Modeling
Zhanyu Ma Jian Ye Shuang Cheng
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.