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Comparing HMMs and Bayesian Networks for Surface Realisation
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@MISC{Dethlefs_comparinghmms,
author = {Nina Dethlefs and Heriberto Cuayáhuitl},
title = {Comparing HMMs and Bayesian Networks for Surface Realisation},
year = {}
}
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Abstract
Natural Language Generation (NLG) systems often use a pipeline architecture for sequential decision making. Recent studies however 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 Hierarchical 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 satisfaction and naturalness. While the former perform best in isolation, the latter present a scalable alternative within joint systems. 1
Keyphrases
bayesian network surface realisation joint learning framework joint system sequential decision making latter present recent study graphical model former perform user satisfaction natural language generation overall performance hierarchical reinforcement learning pipeline architecture scalable alternative nlg decision
