# Denoising Multi-Source Weak Supervision for Neural Text Classification

arXiv: https://arxiv.org/abs/2010.04582 · HTML: https://mlsanity.com/paper/2010.04582/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 30 of 42)

- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction](https://mlsanity.com/paper/2402.13364/) (2024)
- [BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2105.12848/) (2021)
- [Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming](https://mlsanity.com/paper/2203.01382/) (2022)
- [The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators](https://mlsanity.com/paper/2407.11004/) (2024)
- [Understanding Programmatic Weak Supervision via Source-aware Influence Function](https://mlsanity.com/paper/2205.12879/) (2022)
- [Weaker Than You Think: A Critical Look at Weakly Supervised Learning](https://mlsanity.com/paper/2305.17442/) (2023)
- [How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Ranking](https://mlsanity.com/paper/2312.01619/) (2023)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Self-Training with Differentiable Teacher](https://mlsanity.com/paper/2109.07049/) (2021)
- [Meta Self-Refinement for Robust Learning with Weak Supervision](https://mlsanity.com/paper/2205.07290/) (2022)
- [AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels](https://mlsanity.com/paper/2208.14362/) (2022)
- [Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification](https://mlsanity.com/paper/2210.03092/) (2022)
- [Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision](https://mlsanity.com/paper/2210.02724/) (2022)
- [PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble Training](https://mlsanity.com/paper/2305.13723/) (2023)
- [Can Large Language Models Design Accurate Label Functions?](https://mlsanity.com/paper/2311.00739/) (2023)
- [Adaptive Mixing of Auxiliary Losses in Supervised Learning](https://mlsanity.com/paper/2202.03250/) (2022)
- [Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition](https://mlsanity.com/paper/2205.14228/) (2022)
- [SepLL: Separating Latent Class Labels from Weak Supervision Noise](https://mlsanity.com/paper/2210.13898/) (2022)
- [Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal](https://mlsanity.com/paper/2306.04502/) (2023)
- [Learning to Selectively Learn for Weakly-supervised Paraphrase Generation](https://mlsanity.com/paper/2109.12457/) (2021)
- [ScriptoriumWS: A Code Generation Assistant for Weak Supervision](https://mlsanity.com/paper/2502.12366/) (2025)
- [Customer Sentiment Analysis using Weak Supervision for Customer-Agent Chat](https://mlsanity.com/paper/2111.14282/) (2021)
- [Binary Classification with Positive Labeling Sources](https://mlsanity.com/paper/2208.01704/) (2022)
- [WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding](https://mlsanity.com/paper/2108.12603/) (2021)
- [Label Augmentation with Reinforced Labeling for Weak Supervision](https://mlsanity.com/paper/2204.06436/) (2022)
- [ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision](https://mlsanity.com/paper/2204.06863/) (2022)
- [XPASC: Measuring Generalization in Weak Supervision by Explainability and Association](https://mlsanity.com/paper/2206.01444/) (2022)
- [Cross-Platform Hate Speech Detection with Weakly Supervised Causal Disentanglement](https://mlsanity.com/paper/2404.11036/) (2024)

## Abstract (arXiv)

We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challenging because rule-induced weak labels are often noisy and incomplete. To address these two challenges, we design a label denoiser, which estimates the source reliability using a conditional soft attention mechanism and then reduces label noise by aggregating rule-annotated weak labels. The denoised pseudo labels then supervise a neural classifier to predicts soft labels for unmatched samples, which address the rule coverage issue. We evaluate our model on five benchmarks for sentiment, topic, and relation classifications. The results show that our model outperforms state-of-the-art weakly-supervised and semi-supervised methods consistently, and achieves comparable performance with fully-supervised methods even without any labeled data. Our code can be found at https://github.com/weakrules/Denoise-multi-weak-sources.