Script learning studies how daily events unfold. Previous works tend to
consider a script as a linear sequence of events while ignoring the potential
branches that arise due to people's circumstantial choices. We hence propose
Choice-75, the first benchmark that challenges intelligent systems to predict
decisions given descriptive scenarios, containing 75 scripts and more than 600
scenarios. While large language models demonstrate overall decent performances,
there is still notable room for improvement in many hard scenarios.

Script learning studies events and proposes the Choice-75 benchmark to predict decisions in descriptive scenarios, highlighting areas for improvement in language models.

Choice-75: 剧本学习中的决策分支数据集

Choice-75: A Dataset on Decision Branching in Script Learning

Representation and learning of commonsense knowledge is one of the
foundational problems in the quest to enable deep language understanding. This
issue is particularly challenging for understanding casual and correlational
relationships between events. While this topic has received a lot of interest
in the NLP community, research has been hindered by the lack of a proper
evaluation framework. This paper attempts to address this problem with a new
framework for evaluating story understanding and script learning: the 'Story
Cloze Test'. This test requires a system to choose the correct ending to a
four-sentence story. We created a new corpus of ~50k five-sentence commonsense
stories, ROCStories, to enable this evaluation. This corpus is unique in two
ways: (1) it captures a rich set of causal and temporal commonsense relations
between daily events, and (2) it is a high quality collection of everyday life
stories that can also be used for story generation. Experimental evaluation
shows that a host of baselines and state-of-the-art models based on shallow
language understanding struggle to achieve a high score on the Story Cloze
Test. We discuss these implications for script and story learning, and offer
suggestions for deeper language understanding.

本文介绍一个新的 “Story Cloze Test” 框架来测试一个系统对五句子情景的理解和故事学习，同时介绍了用于此框架的新语料库 ROCStories，用于在日常事件之间捕捉因果和时间共识关系的同时收集高质量的日常生活故事。实验证明现有的浅层语言理解模型不能高分通过 Story Cloze 测试，这对剧本和故事的学习提出深层次的理解建议。