Generative Adversarial Imitation Learning for Empathy-based AI
This work addresses the challenge of creating more personalized and empathetic AI interactions, though it is incremental as it builds on existing GAIL and GPT-2 methods.
The paper tackles the problem of generating empathetic responses in conversational AI by applying Generative Adversarial Imitation Learning (GAIL) with a fine-tuned GPT-2 model, resulting in improved performance on human-generated prompts and sustained conversations compared to baseline models.
Generative adversarial imitation learning (GAIL) is a model-free algorithm that has been shown to provide strong results in imitating complex behaviors in high-dimensional environments. In this paper, we utilize the GAIL model for text generation to develop empathy-based context-aware conversational AI. Our model uses an expert trajectory of empathetic prompt-response dialogues which can accurately exhibit the correct empathetic emotion when generating a response. The Generator of the GAIL model uses the GPT-2 sequential pre-trained language model trained on 117 million parameters from 40 GB of internet data. We propose a novel application of an approach used in transfer learning to fine tune the GPT-2 model in order to generate concise, user-specific empathetic responses validated against the Discriminator. Our novel GAIL model utilizes a sentiment analysis history-based reinforcement learning approach to empathetically respond to human interactions in a personalized manner. We find that our model's response scores on various human-generated prompts collected from the Facebook Empathetic Dialogues dataset outperform baseline counterparts. Moreover, our model improves upon various history-based conversational AI models developed recently, as our model's performance over a sustained conversation of 3 or more interactions outperform similar conversational AI models.