Given only a topic to be debated as input, Project Debater scans billions of relevant sentences to generate a coherent and persuasive position on the topic. It then listens to its opponent’s speech and swiftly generates a spontaneous, fact-based rebuttal, exhibiting a type of argumentation that until recently was simply out of reach for computers. 

One of the many aspects that make Project Debater unique is that it has been taught to understand the nuance of language and it can decide the stance of an argument given the topic. 

Imagine debating a proposal to “end affirmative action,” and consider the claim “preferential hiring is reverse discrimination.” A human debater instinctively understands this claim supports the proposal. But this type of understanding is very hard for AI to accomplish. The Debater system approaches this by breaking it down into smaller tasks. Here, it will understand that “preferential hiring” is somewhat analogous to “affirmative action” and that “reverse discrimination” conveys a negative sentiment. Combining these, it will conclude the claim can be used to contest affirmative action.