论文标题
部分可观测时空混沌系统的无模型预测
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
论文作者
论文摘要
尽管关系提取在建筑和代表知识方面的重要性，但较少的研究集中在推广到看不见的关系类型上。我们介绍了零射击关系三重提取（Zerorte）的任务设置，以鼓励以低资源关系提取方法进行进一步的研究。给定输入句子，每个提取的三重态由头部实体，关系标签和尾部实体组成，在训练阶段未看到关系标签。为了解决Zerorte，我们建议通过提示语言模型生成结构化文本来综合关系示例。具体而言，我们将语言模型提示和结构化文本方法统一设计，以设计结构化提示模板，以便在关系标签提示（RelationPrompt）调理时生成合成关系样本。为了克服在句子中提取多个关系三胞胎的限制，我们设计了一种新颖的三重态搜索解码方法。在少数和Wiki-ZSL数据集上进行的实验显示了关系支持对Zerorte任务的功效和零射击关系分类。我们的代码和数据可在github.com/declare-lab/relationprompt上获得。
Despite the importance of relation extraction in building and representing knowledge, less research is focused on generalizing to unseen relations types. We introduce the task setting of Zero-Shot Relation Triplet Extraction (ZeroRTE) to encourage further research in low-resource relation extraction methods. Given an input sentence, each extracted triplet consists of the head entity, relation label, and tail entity where the relation label is not seen at the training stage. To solve ZeroRTE, we propose to synthesize relation examples by prompting language models to generate structured texts. Concretely, we unify language model prompts and structured text approaches to design a structured prompt template for generating synthetic relation samples when conditioning on relation label prompts (RelationPrompt). To overcome the limitation for extracting multiple relation triplets in a sentence, we design a novel Triplet Search Decoding method. Experiments on FewRel and Wiki-ZSL datasets show the efficacy of RelationPrompt for the ZeroRTE task and zero-shot relation classification. Our code and data are available at github.com/declare-lab/RelationPrompt.
