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Developing conversational systems that can converse in many languages is aninteresting challenge for natural language processing. In this paper, weintroduce multilingual addressee and response selection. In this task, aconversational system predicts an appropriate addressee and response for aninput message in multiple languages. A key to developing such multilingualresponding systems is how to utilize high-resource language data to compensatefor low-resource language data. We present several knowledge transfer methodsfor conversational systems. To evaluate our methods, we create a newmultilingual conversation dataset. Experiments on the dataset demonstrate theeffectiveness of our methods.

Addressee and Response Selection for Multilingual Conversation