  Developing conversational systems that can converse in many languages is an
interesting challenge for natural language processing. In this paper, we
introduce multilingual addressee and response selection. In this task, a
conversational system predicts an appropriate addressee and response for an
input message in multiple languages. A key to developing such multilingual
responding systems is how to utilize high-resource language data to compensate
for low-resource language data. We present several knowledge transfer methods
for conversational systems. To evaluate our methods, we create a new
multilingual conversation dataset. Experiments on the dataset demonstrate the
effectiveness of our methods.
