Dialogue act-based partner persona extraction for consistent personalized response generation

The ability of a dialogue model to keep not being out of context during a conversation, so-called keeping the consistency, has long been a critical issue in generating more human-like personalized responses. However, most of the previous works have focused on a self persona to sustain the self consistency during a conversation. Since the consistency is not limited to only the self side, there still lies the problem where generated responses often contradict the utterance of a partner. This kind of behavior discourages the user from responding and eventually causes the user to leave the conversation. To prevent this from happening, our work focuses on recognizing the partner persona. We propose a new model, PEDA (Persona Extractor based on Dialogue Act), and construct an appropriate dataset for training the model. Specifically, dialogue acts are utilized to identify the utterances that capture a user's persona. The proposed model extracts the partner persona from the combination of utterances and their dialogue acts. We propose a dialogue act conductor to properly consider the dialogue act as an input and make use of it with the pre-trained language model. A proposed gating mechanism controls the probability distributions from dialogue act conductor and pre-trained language model. Finally, we train the model further by setting up a reinforcement learning framework with our evaluation network.