论文标题
“这种关系在哪里？”：了解叙事文本中的关系轨迹
"where is this relationship going?": Understanding Relationship Trajectories in Narrative Text
论文作者
论文摘要
我们检查了一项新的常识性推理任务：鉴于描述了以两个主角为中心的社会互动的叙述，系统对基本关系轨迹进行了推断。具体来说，我们提出了两个评估任务：关系Outlook预测MCQ和分辨率预测MCQ。在关系Outlook的预测中，系统将交互映射到关系前景，该相互作用捕获了预期的相互作用如何改变关系。在解决方案预测中，系统将给定的关系前景归因于解释结果的特定分辨率。这两个任务与人们经常思考不同社交场合时经常思考的两个现实生活问题：“这种关系在哪里？”和“我们是怎么到这里的？”。为了通过这两项任务来促进对人类社会关系的调查，我们构建了一个新的数据集，社会叙事树，其中包括1250层，记录各种日常社会互动。叙述编码了许多社会要素，这些社会要素交织在一起，从而引起人们对关系如何在社会互动中发展的丰富认识。我们使用语言模型建立基线表现，精度明显低于人类表现。结果表明，模型需要超越句法和语义信号才能理解复杂的人际关系。
We examine a new commonsense reasoning task: given a narrative describing a social interaction that centers on two protagonists, systems make inferences about the underlying relationship trajectory. Specifically, we propose two evaluation tasks: Relationship Outlook Prediction MCQ and Resolution Prediction MCQ. In Relationship Outlook Prediction, a system maps an interaction to a relationship outlook that captures how the interaction is expected to change the relationship. In Resolution Prediction, a system attributes a given relationship outlook to a particular resolution that explains the outcome. These two tasks parallel two real-life questions that people frequently ponder upon as they navigate different social situations: "where is this relationship going?" and "how did we end up here?". To facilitate the investigation of human social relationships through these two tasks, we construct a new dataset, Social Narrative Tree, which consists of 1250 stories documenting a variety of daily social interactions. The narratives encode a multitude of social elements that interweave to give rise to rich commonsense knowledge of how relationships evolve with respect to social interactions. We establish baseline performances using language models and the accuracies are significantly lower than human performance. The results demonstrate that models need to look beyond syntactic and semantic signals to comprehend complex human relationships.
