# Semi-supervised Relation Extraction via Incremental Meta Self-Training

arXiv: https://arxiv.org/abs/2010.16410 · HTML: https://mlsanity.com/paper/2010.16410/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 30 of 35)

- [Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction](https://mlsanity.com/paper/2109.06415/) (2021)
- [HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction](https://mlsanity.com/paper/2205.02225/) (2022)
- [Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution](https://mlsanity.com/paper/2210.17004/) (2022)
- [Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph](https://mlsanity.com/paper/2208.03903/) (2022)
- [Unsupervised Entity Alignment for Temporal Knowledge Graphs](https://mlsanity.com/paper/2302.00796/) (2023)
- [Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation](https://mlsanity.com/paper/2203.02135/) (2022)
- [Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence](https://mlsanity.com/paper/2305.03507/) (2023)
- [Pair-Level Supervised Contrastive Learning for Natural Language Inference](https://mlsanity.com/paper/2201.10927/) (2022)
- [GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks](https://mlsanity.com/paper/2305.16663/) (2023)
- [Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks](https://mlsanity.com/paper/2402.12411/) (2024)
- [A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference](https://mlsanity.com/paper/2205.15550/) (2022)
- [Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer](https://mlsanity.com/paper/2305.12761/) (2023)
- [Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis](https://mlsanity.com/paper/2305.16166/) (2023)
- [Graph Component Contrastive Learning for Concept Relatedness Estimation](https://mlsanity.com/paper/2206.12556/) (2022)
- [Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks](https://mlsanity.com/paper/2210.10343/) (2022)
- [Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction](https://mlsanity.com/paper/2305.03503/) (2023)
- [TINKER: A framework for Open source Cyberthreat Intelligence](https://mlsanity.com/paper/2102.05571/) (2021)
- [Give Me More Details: Improving Fact-Checking with Latent Retrieval](https://mlsanity.com/paper/2305.16128/) (2023)
- [Targeted Augmentation for Low-Resource Event Extraction](https://mlsanity.com/paper/2405.08729/) (2024)
- [Improve Event Extraction via Self-Training with Gradient Guidance](https://mlsanity.com/paper/2205.12490/) (2022)
- [Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition](https://mlsanity.com/paper/2305.07266/) (2023)
- [Scene Graph Modification as Incremental Structure Expanding](https://mlsanity.com/paper/2209.09093/) (2022)
- [Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation](https://mlsanity.com/paper/2305.15872/) (2023)
- [Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing](https://mlsanity.com/paper/2306.04480/) (2023)
- [Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data](https://mlsanity.com/paper/2306.09697/) (2023)
- [Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths](https://mlsanity.com/paper/2310.05364/) (2023)
- [ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction](https://mlsanity.com/paper/2305.15374/) (2023)
- [Semi-supervised Relation Extraction via Data Augmentation and Consistency-training](https://mlsanity.com/paper/2306.10153/) (2023)
- [TECO: Improving Multimodal Intent Recognition with Text Enhancement through Commonsense Knowledge Extraction](https://mlsanity.com/paper/2412.08529/) (2024)
- [A systematic review of relation extraction task since the emergence of Transformers](https://mlsanity.com/paper/2511.03610/) (2025)

## Abstract (arXiv)

To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods suffer from the gradual drift problem, where noisy pseudo labels on unlabeled data are incorporated during training. To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. To reduce the influence of noisy pseudo labels, MetaSRE adopts a pseudo label selection and exploitation scheme which assesses pseudo label quality on unlabeled samples and only exploits high-quality pseudo labels in a self-training fashion to incrementally augment labeled samples for both robustness and accuracy. Experimental results on two public datasets demonstrate the effectiveness of the proposed approach.