  This paper investigates the use of Machine Translation (MT) to bootstrap a
Natural Language Understanding (NLU) system for a new language for the use case
of a large-scale voice-controlled device. The goal is to decrease the cost and
time needed to get an annotated corpus for the new language, while still having
a large enough coverage of user requests. Different methods of filtering MT
data in order to keep utterances that improve NLU performance and
language-specific post-processing methods are investigated. These methods are
tested in a large-scale NLU task with translating around 10 millions training
utterances from English to German. The results show a large improvement for
using MT data over a grammar-based and over an in-house data collection
baseline, while reducing the manual effort greatly. Both filtering and
post-processing approaches improve results further.
