  To deploy a spoken language understanding (SLU) model to a new language,
language transferring is desired to avoid the trouble of acquiring and labeling
a new big SLU corpus. Translating the original SLU corpus into the target
language is an attractive strategy. However, SLU corpora consist of plenty of
semantic labels (slots), which general-purpose translators cannot handle well,
not to mention additional culture differences. This paper focuses on the
language transferring task given a tiny in-domain parallel SLU corpus. The
in-domain parallel corpus can be used as the first adaptation on the general
translator. But more importantly, we show how to use reinforcement learning
(RL) to further finetune the adapted translator, where translated sentences
with more proper slot tags receive higher rewards. We evaluate our approach on
Chinese to English language transferring for SLU systems. The experimental
results show that the generated English SLU corpus via adaptation and
reinforcement learning gives us over 97% in the slot F1 score and over 84%
accuracy in domain classification. It demonstrates the effectiveness of the
proposed language transferring method. Compared with naive translation, our
proposed method improves domain classification accuracy by relatively 22%, and
the slot filling F1 score by relatively more than 71%.
