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.
@article{arxiv.1808.03915,
title = {Addressee and Response Selection for Multilingual Conversation},
author = {Motoki Sato and Hiroki Ouch and Yuta Tsuboi},
journal= {arXiv preprint arXiv:1808.03915},
year = {2018}
}