W04-2302 on dialogue management through rule-based generation ( Allen et al. , 2001 ) . 4.1
W01-1402 LF alignment . 5.4 Generation A rule-based generation component maps from the target
W00-0306 Introduction Several general-purpose rule-based generation systems have been developed ,
W01-1406 form an output LF . From this , a rule-based generation component produces an output
W13-2809 target language model . Using a rule-based generation component made it difficult to
W00-0306 takes advantages of templates and rule-based generation as needed by specific sentences
P08-1020 extreme traits . 3.3 Comparison with Rule-Based Generation PERSONAGE is a rule-based personality
W04-2302 the naturalness of the output . Rule-based generation has developed as an alternative
J03-1003 graph reformulations of well-known rule-based generation algorithms with stochastic cost
A94-1038 using translation examples . The rule-based generation part of the integrated method
W05-1626 advantageous when relatively shallow but rule-based generation capabilities are required . <title>
D12-1085 for certain languages , work on rule-based generation has addressed certain aspects
W02-2104 ( Aikawa et al. , 2001 ) , the rule-based generation module generates the surface
P15-1006 2012 ) , which are used to drive rule-based generation sys - tems . Regrettably , the
A94-1038 rulebased generation model . 3 Rule-based generation 3.1 Composition The composition
N07-1059 While our approach of using a rule-based generation system gives the developer great
W02-2104 an otherwise totally hand-coded rule-based generation module ; &#8226; To evaluate
E03-1004 lexical transfer , and simple rule-based generation from the tectogrammatical representa
W05-1614 generators assume a relatively simple rule-based generation architecture , but the way that
N01-1001 propose to tightly interleave rule-based generation and ranking rather than employing
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