Natural Language Generation Using Reinforcement Learning with External Rewards
This work addresses language generation for dialogue systems, but it is incremental as it builds on existing RL and attention methods.
The authors tackled natural language generation by incorporating external rewards via reinforcement learning into a bidirectional encoder-decoder, achieving improved performance on dialogue corpora as measured by BLEU, ROUGE-L, perplexity, and human evaluation.
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.