Generating Sentence Planning Variations for Story Telling
This work addresses the limitation of handcrafted NLG components in dialogue and story systems, enabling more dynamic and varied language generation, though it is incremental as it builds on existing methods.
The authors tackled the problem of handcrafted natural language generation in interactive story systems by developing a parameterized sentence planner for story generation, which was evaluated on personal narratives from weblogs and showed improvements in generating variations.
There has been a recent explosion in applications for dialogue interaction ranging from direction-giving and tourist information to interactive story systems. Yet the natural language generation (NLG) component for many of these systems remains largely handcrafted. This limitation greatly restricts the range of applications; it also means that it is impossible to take advantage of recent work in expressive and statistical language generation that can dynamically and automatically produce a large number of variations of given content. We propose that a solution to this problem lies in new methods for developing language generation resources. We describe the ES-Translator, a computational language generator that has previously been applied only to fables, and quantitatively evaluate the domain independence of the EST by applying it to personal narratives from weblogs. We then take advantage of recent work on language generation to create a parameterized sentence planner for story generation that provides aggregation operations, variations in discourse and in point of view. Finally, we present a user evaluation of different personal narrative retellings.