Many classical fairy tales, fiction, and screenplays leverage dialogue to advance story plots and establish characters. We present the first study to explore whether machines can understand and generate dialogue in stories, which requires capturing traits of different characters and the relationships between them. To this end, we propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition, i.e., generating missing dialogue turns and predicting speakers for specified dialogue turns, respectively. We build a new dataset DialStory, which consists of 105k Chinese stories with a large amount of dialogue weaved into the plots to support the evaluation. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory. Furthermore, we propose to learn explicit character representations to improve performance on these tasks. Extensive experiments and case studies show that our approach can generate more coherent and informative dialogue, and achieve higher speaker recognition accuracy than strong baselines.
翻译:许多古典童话故事、小说和剧本都利用对话来推进故事情节和确立字符。 我们提出第一项研究,探讨机器是否能够理解和产生故事对话,这要求捕捉不同字符的特点和它们之间的关系。 为此,我们提出两项新任务,包括蒙面对话的产生和对话发言人的认可,即分别产生缺失的对话转弯和预测特定对话转弯的演讲者。我们建造了一个新的数据集拨号,由105k中国故事组成,大量对话被编入剧中以支持评价。我们通过自动和手工评估《拨号故事》测试现有模型,显示了拟议任务的难度。此外,我们提议学习明确的性格表述,以改进这些任务的业绩。广泛的实验和案例研究表明,我们的方法可以产生更加连贯、更丰富信息的对话,并实现比强的基线更高的语音识别准确性。