We introduce a language generation task grounded in a popular video game environment. KNUDGE (KNowledge Constrained User-NPC Dialogue GEneration) involves generating dialogue trees conditioned on an ontology captured in natural language passages providing quest and entity specifications. KNUDGE is constructed from side quest dialogues drawn directly from game data of Obsidian Entertainment's The Outer Worlds, leading to real-world complexities in generation: (1) dialogues are branching trees as opposed to linear chains of utterances; (2) utterances must remain faithful to the game lore--character personas, backstories, and entity relationships; and (3) a dialogue must accurately reveal new quest-related details to the human player. We report results for supervised and in-context learning techniques, finding there is significant room for future work on creating realistic game-quality dialogues.
翻译:我们引入了一个基于流行的视频游戏环境的语言生成任务。 KNUDGE(KNULEGE (KNULEGE Constraced Under-NPC Expect Generation ) 涉及创造以自然语言通道所捕捉的本体学为条件的对话树,提供搜索和实体规格。 KNUDGE 是由直接从Obsidian娱乐业的游戏数据“外表世界”中提取的边探索性对话构建的,这导致了现实世界的生成的复杂性:(1) 对话是树的分支,而不是线性连锁的表达;(2) 言论必须始终忠实于游戏的客体、后台和实体关系;(3) 对话必须准确地向人类玩家披露与探索相关的新细节。 我们报告监督和文字学习技术的结果,发现今后创造现实的游戏质量对话有相当大的空间。