“这段关系将走向何方？“：理解叙事文本中的关系轨迹
"where is this relationship going?": Understanding Relationship Trajectories in Narrative Text
摘要：
我们研究了一个新的常识推理任务：给定一个描述以两个主角为中心的社会互动的叙述，系统对潜在的关系轨迹进行推断。具体来说，我们提出了两个评估任务：关系前景预测MCQ和分辨率预测MCQ。在关系前景预测中，系统将交互映射到关系前景，该关系前景捕获期望交互如何改变关系。在解决预测中，系统将给定的关系前景归因于解释结果的特定解决方案。这两项任务与现实生活中人们在不同社交场合中经常思考的两个问题相似：“这段关系将走向何方？“和“我们怎么会在这里结束？".为了通过这两项任务促进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，它由1250个故事组成，记录了各种日常社会互动。这些叙述包含了大量的社会元素，这些元素交织在一起，产生了丰富的常识性知识，即关系如何随着社会互动而演变。我们使用语言模型建立基线性能，准确率明显低于人类性能。结果表明，模型需要超越句法和语义信号来理解复杂的人际关系。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
