DEXTOR: Reduced Effort Authoring for Template-Based Natural Language Generation (original) (raw)
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natural language Generation (nlG) systems can make data accessible in an easily digestible textual form; but using such systems requires sophisticated linguistic and sometimes even programming knowledge. We have designed and implemented an environment for creating and modifying nlG templates that requires no programming knowledge, and can operate with a minimum of linguistic knowledge. it allows specifying templates with any number of variables and dependencies between them. it internally uses an existing sentence realization nlG tool in order to provide the linguistic background knowledge. We tested the performance and usability of our system in the context of interactive simulation games. We incrementally improved our system in order to obtain all the capabilities needed to reproduce all the sentences and templates manually created for already existing games. We trained the users and measured their satisfaction with the system by comparing the results of writing new games' narrative content manually vs. using our system. in general, the use of the system made the task faster, more enjoyable, and less prone to errors.
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Natural language generation (NLG) has been featured in at most a handful of shipped games and interactive stories. This is certainly due to it being a very specialized practice, but another contributing factor is that the state of the art today, in terms of content quality, is simply inadequate. The major benefits of NLG are its alleviation of authorial burden and the capability it gives to a system of generating state-bespoke content, but we believe we can have these benefits without actually employing a full NLG pipeline. In this paper, we present the preliminary design of Expressionist, an in-development mixed-initiative authoring tool that instantiates an authoring scheme residing somewhere between conventional NLG and conventional human content authoring. In this scheme, a human author plays the part of an NLG module in that she starts from a set of deep representations constructed for the game or story domain and proceeds to specify dialogic content that may express those representations. Rather than authoring static dialogue, the author defines a probabilistic context-free grammar that yields templated dialogue. This allows a human author to still harness a computer's generativity, but in a capacity in which it can be trusted: operating over probabilities and treelike control structures. Additional features of Expressionist's design include arbitrary markup and realtime feedback showing currently valid derivations.
Novice-Friendly Natural Language Generation Template Authoring Environment
Advances in Artificial …, 2009
Natural Language Generation (NLG) systems can make data accessible in an easily digestible textual form; but using such systems requires sophisticated linguistic and sometimes even programming knowledge. We have designed and implemented an environment for creating and modifying NLG templates that requires no programming knowledge, and can operate with a minimum of linguistic knowledge. It allows specifying templates with any number of variables and dependencies between them. It internally uses Sim-pleNLG to provide the linguistic background knowledge. We test the performance of our system in the context of an interactive simulation game.
Automatic generation of narrative content for digital games
2009
Interactive simulation games used for training usually require a large amount of coherent narrative content. An effective and efficient solution to the narrative content creation problem is to use Natural Language Generation (NLG) systems. The use of NLG systems, however, requires sophisticated linguistic and sometimes programming knowledge. For this reason, NLG systems are typically not accessible to the game designers who write narrative content. We have designed and implemented a visual environment for creating and modifying NLG templates that requires no programming knowledge, and can operate with a minimum of linguistic knowledge. It allows specifying templates with any number of variables and dependencies between them. It automatically generates all the sentences that follow the created template. It uses SimpleNLG to provide the linguistic background knowledge. We tested the performance of our system in the context of an interactive simulation game.
Visual development process for automatic generation of digital games narrative content
Proceedings of the 2009 Workshop on Language Generation and Summarisation - UCNLG+Sum '09, 2009
Users of Natural Language Generation systems are required to have sophisticated linguistic and sometimes even programming knowledge, which has hindered the adoption of this technology by individuals outside the computational linguistics research community. We have designed and implemented a visual environment for creating and modifying NLG templates which requires no programming ability and minimum linguistic knowledge. It allows specifying templates with any number of variables and dependencies between them. Internally, it uses SimpleNLG to provide the linguistic background knowledge. We tested the performance of our system in the context of an interactive simulation game. We describe the templates used for testing and show examples of sentences that our system generates from these templates.
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We present a practical approach to Natural Language Generation (NLG) for spoken dialogue systems. The approach is based on small template fragments (mini-templates). The system's object architecture facilitates generation of phrases across pre-defined business domains and registers, as well as into different languages. The architecture simplifies NLG in well-understood application contexts, while providing the flexibility for a developer and for the system, to vary linguistic output according to dialogue context, including any intended affective impact. Mini-templates are used with a suite of domain term objects, resulting in an NLG system (MINTGEN-MINi-Template GENerator) whose extensibility and ease of maintenance is enhanced by the sparsity of information devoted to individual domains. The system also avoids the need for specialist linguistic competence on the part of the system maintainer.
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Dialogue authoring in large games requires not only content creation but the subtlety of its delivery, which can vary from character to character. Manually authoring this dialogue can be tedious, time-consuming, or even altogether infeasible. This paper utilizes a rich narrative representation for modeling dialogue and an expressive natural language generation engine for realizing it, and expands upon a translation tool that bridges the two. We add functionality to the translator to allow direct speech to be modeled by the narrative representation, whereas the original translator supports only narratives told by a third person narrator. We show that we can perform character substitution in dialogues. We implement and evaluate a potential application to dialogue implementation: generating dialogue for games with big, dynamic, or procedurally-generated open worlds. We present a pilot study on human perceptions of the personalities of characters using direct speech, assuming unknown personality types at the time of authoring.
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One compelling aspect of computer RPGs is the promise of player agency: the ability to make significant and desired choices in a large, complex, and story-rich environment. Giving players meaningful choice has traditionally required the creation of tremendous amounts of hand-authored story content. This authoring paradigm tends to introduce both structural and workload problems for RPG designers. Our hypothesis is that reducing authorial burden and increasing agency are two sides of the same coin, both requiring advancement in three distinct areas: (1) dynamic story management architecture that allows story elements to be selected and re-ordered in response to player choices; (2) dynamic dialogue generation which takes history and relationships into account; and (3) an authoring interface that lets writers focus on quests and characters. This paper describes SpyFeet, a playable prototype of a storytelling system designed to test this hypothesis.
Supporting Dialogue Generation for Story-Based Games
Providing compelling, realistic, immersive game worlds is one of the major goals in modern game design. The presence of unique and interesting dialogue for all of the characters in a game enhances this sense of immersion. In this paper we present the concept of an "Intentional Dialogue Line" that supports the efficient generation of multiple variations of a dialogue, where these variations are both unique and appropriate to the character who is speaking them. This paper focuses on how machine learning can be used to quickly populate the intentional dialogue lines with existing content.
Handbook of Natural Language Processing, 2000
We report here on a significant new set of capabilities that we have incorporated into our language generation system MUMBLE. Their impact will be to greatly simplify the work of any text planner that uses MUMBLE as ita linguistics component since MUMBLE can now take on many of the planner's text organization and decision-making problems with markedly less hand-tailoring of algorithms in either component.