Paper: A Decision-Theoretic Approach to Natural Language Generation

ACL ID P14-1052
Title A Decision-Theoretic Approach to Natural Language Generation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We study the problem of generating an En- glish sentence given an underlying prob- abilistic grammar, a world and a com- municative goal. We model the genera- tion problem as a Markov decision process with a suitably defined reward function that reflects the communicative goal. We then use probabilistic planning to solve the MDP and generate a sentence that, with high probability, accomplishes the com- municative goal. We show empirically that our approach can generate complex sen- tences with a speed that generally matches or surpasses the state of the art. Further, we show that our approach is anytime and can handle complex communicative goals, including negated goals.