Inspired by IBM Project Debater, we aim to uncover the myth of persuasive debate with major focus on text data. Using NLP tools such as sentiment analysis and representation learning, we can extract features from debate texts and study constituents of persuasive speeches and similarities among good/bad debaters. Finally, for each user to improve accordingly, a summary with persuasiveness scores after each sentence will be generated in real time. Thus, the product offers an alternative for debaters to practice their expertise with minimal cost and time. If time permits, we will incorporate tone changes in audio and facial expressions in video to extract features beyond texts that affect the persuasiveness of debate. As claps and long time silence in the audio can function as real time supervisions, animated simulations can be generated to track the performance of speakers on timeline. Our model can thus generate more comprehensive advice of managing emotional expression in the debate.
Authors: Xuanyu Wu, Bo Zhang, Yifei Ning, Mao Li Supervisor: Prof. Jingbo Shang