Abstract
Zero-shot relation extraction aims to identify relations between entity mentions using textual descriptions of novel types (i.e., previously unseen) instead of labeled training examples. Previous works often rely on unrealistic assumptions: (1) pairs of mentions are often encoded directly in the input, which prevents offline pre-computation for large scale document database querying; (2) no rejection mechanism is introduced, biasing the evaluation when using these models in a retrieval scenario where some (and often most) inputs are irrelevant and must be ignored. In this work, we study the robustness of existing zero-shot relation extraction models when adapting them to a realistic extraction scenario. To this end, we introduce a typology of existing models, and propose several strategies to build single pass models and models with a rejection mechanism. We adapt several state-of-the-art tools, and compare them in this challenging setting, showing that no existing work is really robust to realistic assumptions, but overall AlignRE (Li et al., 2024) performs best along all criteria.
Abstract (translated)
零样本关系抽取的目标是利用文本描述的新类型（即之前未见过的）来识别实体提及之间的关系，而不是依赖于有标签的训练示例。以往的工作常常基于一些不切实际的假设：(1) 实体对通常直接编码在输入中，这阻碍了大规模文档数据库查询时的离线预计算；(2) 没有引入拒绝机制，在检索场景下使用这些模型时会偏向于评估，因为在某些情况下（通常是大多数情况），输入是无关的且必须被忽略。在这项工作中，我们研究了现有零样本关系抽取模型在适应现实提取场景时的稳健性。为此，我们介绍了现有模型的分类，并提出了几种策略来构建单次通过模型和具备拒绝机制的模型。我们将几款最先进的工具进行了调整，在这种具有挑战性的设置下进行比较，结果显示目前没有工作能真正应对现实假设的挑战，但总体而言，AlignRE（Li等，2024）在所有标准上表现最佳。
URL
https://arxiv.org/abs/2603.01266
