提升零样本关系抽取在真实场景下的稳定性与效率。
A Study on Building Efficient Zero-Shot Relation Extraction Models
- 提出单次遍历和拒绝机制，更适配真实检索场景。
- 现有模型均不鲁棒，AlignRE在各项指标表现最佳。
- 适合构建高效、可扩展的零样本关系抽取系统。
零样本关系抽取旨在利用新关系类型的文本描述而非标注数据来识别实体提及间的关系。以往方法常依赖不切实际的假设：(1) 实体对通常直接编码于输入中，无法对大规模文档库进行离线预计算；(2) 缺乏拒绝机制，在检索场景中若多数输入无关却未被忽略，将导致评估偏差。本文研究现有零样本关系抽取模型在真实提取场景中的鲁棒性，提出模型分类体系，并设计多种策略构建单次遍历模型与带拒绝机制的模型。我们适配多个前沿工具，在该挑战性设置下进行对比，结果表明：无一现有工作真正适应现实假设，但总体上 AlignRE (Li et al., 2024) 在所有标准下表现最优。
原文摘要 · Abstract (English)
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.
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