# Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification

arXiv: https://arxiv.org/abs/2205.13139 · HTML: https://mlsanity.com/paper/2205.13139/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 15 of 15)

- [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)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation](https://mlsanity.com/paper/1809.09478/) (2018)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [Unbalanced minibatch Optimal Transport; applications to Domain Adaptation](https://mlsanity.com/paper/2103.03606/) (2021)
- [Adversarial-Learned Loss for Domain Adaptation](https://mlsanity.com/paper/2001.01046/) (2020)
- [A Corpus for Multilingual Document Classification in Eight Languages](https://mlsanity.com/paper/1805.09821/) (2018)
- [PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models](https://mlsanity.com/paper/2006.09075/) (2020)
- [Bi-Directional Generation for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.04869/) (2020)
- [Unsupervised Domain Adaptation for Semantic Segmentation by Content Transfer](https://mlsanity.com/paper/2012.12545/) (2020)
- [CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2008.10464/) (2020)
- [Domain Adversarial Reinforcement Learning for Partial Domain Adaptation](https://mlsanity.com/paper/1905.04094/) (2019)

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

- [The Moral Foundations Reddit Corpus](https://mlsanity.com/paper/2208.05545/) (2022)
- [Token Imbalance Adaptation for Radiology Report Generation](https://mlsanity.com/paper/2304.09185/) (2023)

## Abstract (arXiv)

Class imbalance naturally exists when train and test models in different domains. Unsupervised domain adaptation (UDA) augments model performance with only accessible annotations from the source domain and unlabeled data from the target domain. However, existing state-of-the-art UDA models learn domain-invariant representations and evaluate primarily on class-balanced data across domains. In this work, we propose an unsupervised domain adaptation approach via reinforcement learning that jointly leverages feature variants and imbalanced labels across domains. We experiment with the text classification task for its easily accessible datasets and compare the proposed method with five baselines. Experiments on three datasets prove that our proposed method can effectively learn robust domain-invariant representations and successfully adapt text classifiers on imbalanced classes over domains. The code is available at https://github.com/woqingdoua/ImbalanceClass.