  This work proposes a novel approach based on sequence-to-sequence (seq2seq)
models for context-aware conversational systems. Exist- ing seq2seq models have
been shown to be good for generating natural responses in a data-driven
conversational system. However, they still lack mechanisms to incorporate
previous conversation turns. We investigate RNN-based methods that efficiently
integrate previous turns as a context for generating responses. Overall, our
experimental results based on human judgment demonstrate the feasibility and
effectiveness of the proposed approach.
