Finding Influential Instances for Distantly Supervised Relation Extraction

Abstract

Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise. Current works based on attention, reinforcement learning, or GAN are black-box models so they neither provide meaningful interpretation of sample selection in DS nor stability on different domains. On the contrary, this work proposes a novel model-agnostic instance sampling method for DS by influence function (IF), namely REIF. Our method identifies favorable/unfavorable instances in the bag based on IF, then does dynamic instance sampling. We design a fast influence sampling algorithm that reduces the computational complexity from $\mathcal{O}(mn)$ to $\mathcal{O}(1)$, with analyzing its robustness on the selected sampling function. Experiments show that by simply sampling the favorable instances during training, REIF is able to win over a series of baselines that have complicated architectures. We also demonstrate that REIF can support interpretable instance selection.

References in corpus (3)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• ADADELTA: An Adaptive Learning Rate Method
• Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training

Cited by in corpus (7)

• KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
• Learning from Noisy Labels for Entity-Centric Information Extraction
• Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
• Document-level Relation Extraction as Semantic Segmentation
• PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
• Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
• Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction