  Neural network-based Open-ended conversational agents automatically generate
responses based on predictive models learned from a large number of pairs of
utterances. The generated responses are typically acceptable as a sentence but
are often dull, generic, and certainly devoid of any emotion. In this paper, we
present neural models that learn to express a given emotion in the generated
response. We propose four models and evaluate them against 3 baselines. An
encoder-decoder framework-based model with multiple attention layers provides
the best overall performance in terms of expressing the required emotion. While
it does not outperform other models on all emotions, it presents promising
results in most cases.
