Using reinforcement learning with external rewards for open-domain natural language generation

We propose a new approach towards emotional natural language generation using bidirectional seq2seq model. Our goal is to generate emotionally relevant language that accommodates the emotional tone of the prior context. To incorporate emotional information, we train our own embeddings appended with emotion values through valence, arousal and dominance scores. We use a reinforcement-learning framework, which is tuned using policy gradient method. Two of the internal rewards in our reinforcement learning framework, viz. Ease of Answering and Semantic Coherence are based on prior state-of-the-art. We propose a new internal reward, Emotional Intelligence, computed by minimizing the affective dissonance between the source and generated text. We also train a separate external reward analyzer to predict the rewards as well as to maximize the expected rewards (both internal and external). We evaluate the system on two common corpora used for Natural Language Generation tasks: the Cornell Movie Dialog 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. We demonstrate the ability of proposed model to generate emotionally appropriate responses on both corpora.