  We propose an approach towards natural language generation using a
bidirectional encoder-decoder which incorporates external rewards through
reinforcement learning (RL). We use attention mechanism and maximum mutual
information as an initial objective function using RL. Using a two-part
training scheme, we train an external reward analyzer to predict the external
rewards and then use the predicted rewards to maximize the expected rewards
(both internal and external). We evaluate the system on two standard dialogue
corpora - Cornell Movie Dialog Corpus and Yelp Restaurant Review Corpus. We
report standard evaluation metrics including BLEU, ROUGE-L, and perplexity as
well as human evaluation to validate our approach.
