  Existing dialog systems are all monolingual, where features shared among
different languages are rarely explored. In this paper, we introduce a novel
multilingual dialogue system. Specifically, we augment the sequence to sequence
framework with improved shared-private memory. The shared memory learns common
features among different languages and facilitates a cross-lingual transfer to
boost dialogue systems, while the private memory is owned by each separate
language to capture its unique feature. Experiments conducted on Chinese and
English conversation corpora of different scales show that our proposed
architecture outperforms the individually learned model with the help of the
other language, where the improvement is particularly distinct when the
training data is limited.
