  Encoder-decoder based neural architectures serve as the basis of
state-of-the-art approaches in end-to-end open domain dialog systems. Since
most of such systems are trained with a maximum likelihood~(MLE) objective they
suffer from issues such as lack of generalizability and the generic response
problem, i.e., a system response that can be an answer to a large number of
user utterances, e.g., "Maybe, I don't know." Having explicit feedback on the
relevance and interestingness of a system response at each turn can be a useful
signal for mitigating such issues and improving system quality by selecting
responses from different approaches. Towards this goal, we present a system
that evaluates chatbot responses at each dialog turn for coherence and
engagement. Our system provides explicit turn-level dialog quality feedback,
which we show to be highly correlated with human evaluation. To show that
incorporating this feedback in the neural response generation models improves
dialog quality, we present two different and complementary mechanisms to
incorporate explicit feedback into a neural response generation model:
reranking and direct modification of the loss function during training. Our
studies show that a response generation model that incorporates these combined
feedback mechanisms produce more engaging and coherent responses in an
open-domain spoken dialog setting, significantly improving the response quality
using both automatic and human evaluation.

Computation and Language;Artificial Intelligence;Machine Learning