In this paper we argue that crime drama exemplified in television programs such as CSI:Crime Scene Investigation is an ideal testbed for approximating real-world natural language understanding and the complex inferences associated with it. We propose to treat crime drama as a new inference task, capitalizing on the fact that each episode poses the same basic question (i.e., who committed the crime) and naturally provides the answer when the perpetrator is revealed. We develop a new dataset based on CSI episodes, formalize perpetrator identification as a sequence labeling problem, and develop an LSTM-based model which learns from multi-modal data. Experimental results show that an incremental inference strategy is key to making accurate guesses as well as learning from representations fusing textual, visual, and acoustic input.

本文认为如CSI犯罪剧等犯罪戏剧是近似于真实世界自然语言理解和相关复杂推理的理想测试平台，因为每一集都提供同一个基本问题（即谁犯了罪），并为这个问题自然地提供答案。该文开发了基于CSI剧集的新数据集，将罪犯识别形式化为序列标记问题，并开发了基于LSTM的模型，该模型从多模式数据中学习，实验结果表明增量推理策略是进行准确猜测以及从融合文本、视觉和声音输入的表示中学习的关键。

侦破案件？犯罪剧作为自然语言理解的案例