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首页> 外文会议>Workshop on Computational Approaches to Discourse >Supporting Comedy Writers: Predicting Audience's Response from Sketch Comedy and Crosstalk Scripts
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Supporting Comedy Writers: Predicting Audience's Response from Sketch Comedy and Crosstalk Scripts

机译：支持喜剧作家：预测观众从素描喜剧和串扰脚本的回应

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摘要

Sketch comedy and crosstalk are two popular types of comedy. They can relieve people's stress and thus benefit their mental health, especially when performances and scripts are high-quality. However, writing a script is time-consuming and its quality is difficult to achieve. In order to minimise the time and effort needed for producing an excellent script, we explore ways of predicting the audience's response from the comedy scripts. For this task, we present a corpus of annotated scripts from popular television entertainment programmes in recent years. Annotations include a) text classification labels, indicating which actor's lines made the studio audience laugh; b) information extraction labels, i.e. the text spans that made the audience laughed immediately after the performers said them. The corpus will also be useful for dialogue systems and discourse analysis, since our annotations are based on entire scripts. In addition, we evaluate different baseline algorithms. Experimental results demonstrate that BERT models can achieve the best predictions among all the baseline methods. Furthermore, we conduct an error analysis and investigate predictions across scripts with different styles.
机译：素描喜剧和串扰是两个流行的喜剧类型。他们可以缓解人们的压力，从而有利于他们的心理健康，特别是当表演和脚本是高质量的。但是，写脚本是耗时的，其质量难以实现。为了最大限度地减少产生优秀脚本所需的时间和精力，我们探讨了预测观众对喜剧脚本的响应的方式。对于此任务，我们近年来介绍了来自流行电视娱乐课程的注释脚本的语料库。注释包括a）文本分类标签，表明哪个演员的线条使工作室观众笑; b）信息提取标签，即使观众在表演者所说之后立即嘲笑的文本跨度。由于我们的注释基于整个脚本，语料库也可用于对话系统和话语分析。此外，我们还评估了不同的基线算法。实验结果表明，BERT模型可以在所有基线方法中实现最佳预测。此外，我们在具有不同风格的脚本上进行错误分析和调查预测。

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