GenPADS: Reinforcing politeness in an end-to-end dialogue system

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Human-Computer Interaction

Background:

• Task-oriented dialogue systems face challenges with user mood and demand shifts, leading to ineffective conversations.
• Conversational agents need to adapt by learning user behavior for informative, empathetic, and interactive responses.

Purpose of the Study:

• To propose and evaluate GenPADS, a novel end-to-end dialogue system designed to address dynamic user needs in task-oriented conversations.
• To enhance conversational agents' ability to generate polite, informative, and semantically correct responses adaptively.

Main Methods:

• Developed a politeness classifier and a generation model (G) for dialogue responses.
• Integrated these models into a reinforcement learning (RL) framework with politeness-oriented reward algorithms.
• Annotated the Taskmaster dataset for politeness classification and created the GenDD dataset for the generator model.

Main Results:

• GenPADS demonstrated superior performance compared to baseline models (transformer-based seq2seq and retrieval-based PADS).
• The system successfully adapted and generated polite responses, improving dialogue quality.
• Evaluations included automatic metrics and human assessments using seven user simulators.

Conclusions:

• GenPADS effectively addresses the challenge of evolving user demands in task-oriented dialogues.
• The proposed system enhances conversational agents' capabilities for informative, empathetic, and polite interactions.
• This work contributes to the development of more adaptive and user-centric dialogue systems.