  How to generate human like response is one of the most challenging tasks for
artificial intelligence. In a real application, after reading the same post
different people might write responses with positive or negative sentiment
according to their own experiences and attitudes. To simulate this procedure,
we propose a simple but effective dual-decoder model to generate response with
a particular sentiment, by connecting two sentiment decoders to one encoder. To
support this model training, we construct a new conversation dataset with the
form of (post, resp1, resp2) where two responses contain opposite sentiment.
Experiment results show that our dual-decoder model can generate diverse
responses with target sentiment, which obtains significant performance gain in
sentiment accuracy and word diversity over the traditional single-decoder
model. We will make our data and code publicly available for further study.
