# KnowMAN: Weakly Supervised Multinomial Adversarial Networks

arXiv: https://arxiv.org/abs/2109.07994 · HTML: https://mlsanity.com/paper/2109.07994/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification](https://mlsanity.com/paper/1606.01614/) (2016)
- [Multinomial Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/1802.05694/) (2018)
- [Improving Relation Extraction by Pre-trained Language Representations](https://mlsanity.com/paper/1906.03088/) (2019)
- [DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction](https://mlsanity.com/paper/1805.09929/) (2018)
- [Knodle: Modular Weakly Supervised Learning with PyTorch](https://mlsanity.com/paper/2104.11557/) (2021)

## Built on by (most cited first, 2 of 2)

- [SepLL: Separating Latent Class Labels from Weak Supervision Noise](https://mlsanity.com/paper/2210.13898/) (2022)
- [XPASC: Measuring Generalization in Weak Supervision by Explainability and Association](https://mlsanity.com/paper/2206.01444/) (2022)

## Abstract (arXiv)

The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels. The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training. This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well. We propose KnowMAN, an adversarial scheme that enables to control influence of signals associated with specific labeling functions. KnowMAN forces the network to learn representations that are invariant to those signals and to pick up other signals that are more generally associated with an output label. KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline.