TVShowGuess: Character Comprehension in Stories as Speaker Guessing
TVShowGuess: Character Comprehension in Stories as Speaker Guessing
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DOI：
10.48550/arxiv.2204.07721
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Yisi Sang;Xiangyang Mou;Mo Yu;Shunyu Yao;Jing Li;Jeffrey Stanton
中科院分区：
文献类型：
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作者：
Yisi Sang;Xiangyang Mou;Mo Yu;Shunyu Yao;Jing Li;Jeffrey Stanton
我们提出了一项新任务来评估机器理解叙事故事中虚构人物的技能。 TVShowGuess 任务以电视剧剧本为基础，采用根据场景背景和对话猜测匿名主角的形式。我们的人类研究表明，这种形式的任务涵盖了对多种类型人物形象的理解，包括理解人物的个性、事实和个人经历的记忆，这与人类在阅读过程中理解虚构人物的心理理论（ToM）的心理学和文学理论非常吻合。我们进一步提出新的模型架构来支持长场景文本的上下文编码。实验表明，我们提出的方法明显优于基线，但仍然很大程度上落后于（近乎完美的）人类表现。我们的工作是实现叙事人物理解目标的第一步。
We propose a new task for assessing machines’ skills of understanding fictional characters in narrative stories. The task, TVShowGuess, builds on the scripts of TV series and takes the form of guessing the anonymous main characters based on the backgrounds of the scenes and the dialogues. Our human study supports that this form of task covers comprehension of multiple types of character persona, including understanding characters’ personalities, facts and memories of personal experience, which are well aligned with the psychological and literary theories about the theory of mind (ToM) of human beings on understanding fictional characters during reading. We further propose new model architectures to support the contextualized encoding of long scene texts. Experiments show that our proposed approaches significantly outperform baselines, yet still largely lag behind the (nearly perfect) human performance.Our work serves as a first step toward the goal of narrative character comprehension.