Understanding Communicative Intentions Using Simulated Role-reversal

Understanding the communicative intention of a speaker is the ultimate goal of language comprehension. Yet, there is very little computational work on this topic. In this chapter a general cognitive plausible model of how an addressee can understand communicative intentions is presented in mathematical detail. The key mechanism of the model is simulated role-reversal of the addressee with the speaker, i.e., the addressee puts himself in the state of the speaker and — using his own experience about plausible intentions — computes the most likely intention in the given context. To show the model’s computational effectiveness, it was implemented in a multi-agent system. In this system agents learn about which states of the world are desirable using a neural network trained with reinforcement learning. The power of simulated role-reversal in understanding communicative intention was demonstrated by depriving the utterances of speakers of all content. Employing the outlined model, the agents nevertheless accomplished a remarkable understanding of intentions using context information alone.