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.

翻译：零样本关系抽取旨在利用新型关系类型（即先前未见过的）的文本描述来识别实体提及之间的关系，而非依赖标注的训练样本。先前研究通常依赖于不切实际的假设：（1）提及对常被直接编码于输入中，这阻碍了大规模文档数据库查询所需的离线预计算；（2）未引入拒绝机制，导致在检索场景中使用这些模型时（其中部分甚至多数输入无关且必须被忽略）评估结果存在偏差。本研究探讨了现有零样本关系抽取模型在适应现实抽取场景时的鲁棒性。为此，我们提出了现有模型的类型学框架，并提出了构建单次遍历模型及具备拒绝机制的模型的若干策略。我们适配了多种先进工具，在此挑战性设定下进行比较，结果表明：尽管现有研究均未完全满足现实假设的鲁棒性要求，但总体而言 AlignRE（Li 等人，2024）在所有评估标准中表现最佳。