Paper: Comparing HMMs and Bayesian Networks for Surface Realisation

ACL ID N12-1081
Title Comparing HMMs and Bayesian Networks for Surface Realisation
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012
Authors

Natural Language Generation (NLG) systems often use a pipeline architecture for sequen- tial decision making. Recent studies how- ever have shown that treating NLG decisions jointly rather than in isolation can improve the overall performance of systems. We present a joint learning framework based on Hierar- chical Reinforcement Learning (HRL) which uses graphical models for surface realisation. Our focus will be on a comparison of Bayesian Networks and HMMs in terms of user satis- faction and naturalness. While the former per- form best in isolation, the latter present a scal- able alternative within joint systems.