Abstract
In natural language generation, the task of Referring Expression Generation (REG) is to determine a set of features or relations which identify a target object. Referring expressions describe the target object and discriminate it from other objects in a scene. From an algorithmic point of view, REG can be posed as a search problem. Since search space is exponential with respect to the number of features and relations available, efficient search strategies are required. In this paper we investigate variants of Monte-Carlo Tree Search (MCTS) for application in REG. We propose a new variant, called Quasi Best-First MCTS (QBF-MCTS). In an empirical study we compare different MCTS variants to one another, and to classic REG algorithms. The results indicate that QBF-MCTS yields a significantly improved performance with respect to efficiency and quality.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
