Abstract
Distantly Supervised Relation Extraction (DSRE) remains a long-standing challenge in NLP, where models must learn from noisy bag-level annotations while making sentence-level predictions. While existing state-of-the-art (SoTA) DSRE models rely on task-specific training, their integration with in-context learning (ICL) using large language models (LLMs) remains underexplored. A key challenge is that the LLM may not learn relation semantics correctly, due to noisy annotation. In response, we propose HYDRE -- HYbrid Distantly Supervised Relation Extraction framework. It first uses a trained DSRE model to identify the top-k candidate relations for a given test sentence, then uses a novel dynamic exemplar retrieval strategy that extracts reliable, sentence-level exemplars from training data, which are then provided in LLM prompt for outputting the final relation(s). We further extend HYDRE to cross-lingual settings for RE in low-resource languages. Using available English DSRE training data, we evaluate all methods on English as well as a newly curated benchmark covering four diverse low-resource Indic languages -- Oriya, Santali, Manipuri, and Tulu. HYDRE achieves up to 20 F1 point gains in English and, on average, 17 F1 points on Indic languages over prior SoTA DSRE models. Detailed ablations exhibit HYDRE's efficacy compared to other prompting strategies.
Abstract (translated)
远监督关系抽取（DSRE）在自然语言处理领域一直是一个长期存在的挑战，模型需要从带有噪声的句子级别标注中学习并进行句级别的预测。尽管现有的最先进的（SoTA）DSRE模型依赖于特定任务的训练，但它们与大规模语言模型（LLMs）中的上下文学习（ICL）集成的研究却相对较少。一个关键的问题是由于标注数据的噪声性，LLM可能无法正确地学习关系语义。为了解决这个问题，我们提出了HYDRE——混合远监督关系抽取框架。该框架首先使用经过训练的DSRE模型来识别给定测试句子中的前k个候选关系，然后采用一种新颖的动态示例检索策略从训练数据中提取可靠、句级别的示例，并将这些示例如实提供在LLM提示中以输出最终的关系。我们进一步扩展了HYDRE框架的应用范围，使其适用于低资源语言的关系抽取跨语言场景。 使用现有的英文DSRE训练数据，我们在英语以及一个新的基准测试上评估了所有方法——该测试涵盖了四种多样的低资源印地语系语言：奥里亚语、桑塔利语、梅泰语和图鲁语。HYDRE在英语上的表现比之前的SoTA DSRE模型高出最多20个F1分数点，并且平均而言，在印地语系语言上也提高了17个F1分数点。详细的消融实验展示了HYDRE相较于其他提示策略的优越性。
URL
https://arxiv.org/abs/2510.18344
