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Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset

机译:对话讲故事:龙与地下城的关键角色

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This paper describes the Critical Role Dungeons and Dragons Dataset (CRD3) and related analyses. Critical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game. The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding abstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player collaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail, and semantic ties to the previous dialogues. In addition, we provide a data augmentation method that produces 34,243 summary-dialogue chunk pairs to support current neural ML approaches, and we provide an abstractive summarization benchmark and evaluation.
机译:本文描述了《地下城与龙》数据集(CRD3)的关键角色和相关分析。《关键角色》是一个无剧本的流媒体直播节目,固定的一群人在其中扮演龙与地下城,这是一个开放式角色扮演游戏。该数据集收集了159个关键角色片段,这些片段被转录成文本对话,包括398682个回合。它还包括从Fandom wiki收集的相应摘要。该数据集在语言上是独一无二的,因为故事完全是通过玩家协作和口头互动生成的。对于每一段对话,都有大量的转折点、多个抽象的摘要和不同程度的细节,以及与之前对话的语义联系。此外,我们还提供了一种数据扩充方法,生成34243个摘要对话块对,以支持当前的神经ML方法,并提供了一个抽象摘要基准和评估。

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