tech,0-1-N01-1003,bq </term> and <term> key prediction </term> . <term> Sentence planning </term> is a set of inter-related but distinct
tech,15-1-N01-1003,bq but distinct tasks , one of which is <term> sentence scoping </term> , i.e. the choice of <term> syntactic
other,22-1-N01-1003,bq scoping </term> , i.e. the choice of <term> syntactic structure </term> for elementary <term> speech acts </term>
other,26-1-N01-1003,bq syntactic structure </term> for elementary <term> speech acts </term> and the decision of how to combine
other,40-1-N01-1003,bq how to combine them into one or more <term> sentences </term> . In this paper , we present <term>
tool,6-2-N01-1003,bq </term> . In this paper , we present <term> SPoT </term> , a <term> sentence planner </term> ,
tech,9-2-N01-1003,bq paper , we present <term> SPoT </term> , a <term> sentence planner </term> , and a new methodology for automatically
tool,19-2-N01-1003,bq methodology for automatically training <term> SPoT </term> on the basis of <term> feedback </term>
other,27-2-N01-1003,bq of <term> feedback </term> provided by <term> human judges </term> . We reconceptualize the task into
tech,6-4-N01-1003,bq distinct phases . First , a very simple , <term> randomized sentence-plan-generator ( SPG ) </term> generates a potentially large list
other,18-4-N01-1003,bq potentially large list of possible <term> sentence plans </term> for a given <term> text-plan input </term>
other,23-4-N01-1003,bq <term> sentence plans </term> for a given <term> text-plan input </term> . Second , the <term> sentence-plan-ranker
tech,3-5-N01-1003,bq text-plan input </term> . Second , the <term> sentence-plan-ranker ( SPR ) </term> ranks the list of output <term> sentence
other,12-5-N01-1003,bq SPR ) </term> ranks the list of output <term> sentence plans </term> , and then selects the top-ranked
other,20-5-N01-1003,bq </term> , and then selects the top-ranked <term> plan </term> . The <term> SPR </term> uses <term> ranking
tech,1-6-N01-1003,bq the top-ranked <term> plan </term> . The <term> SPR </term> uses <term> ranking rules </term> automatically
model,3-6-N01-1003,bq plan </term> . The <term> SPR </term> uses <term> ranking rules </term> automatically learned from <term> training
lr,8-6-N01-1003,bq rules </term> automatically learned from <term> training data </term> . We show that the trained <term> SPR
tech,5-7-N01-1003,bq data </term> . We show that the trained <term> SPR </term> learns to select a <term> sentence
other,10-7-N01-1003,bq <term> SPR </term> learns to select a <term> sentence plan </term> whose <term> rating </term> on average
hide detail