# Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification

arXiv: https://arxiv.org/abs/1809.00530 · HTML: https://mlsanity.com/paper/1809.00530/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2018.

## Built on by (most cited first, 16 of 16)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning](https://mlsanity.com/paper/2311.11551/) (2023)
- [Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach](https://mlsanity.com/paper/2107.12866/) (2021)
- [Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification](https://mlsanity.com/paper/2205.08772/) (2022)
- [Unsupervised Non-transferable Text Classification](https://mlsanity.com/paper/2210.12651/) (2022)
- [Domain Confused Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.04564/) (2022)
- [Topic Driven Adaptive Network for Cross-Domain Sentiment Classification](https://mlsanity.com/paper/2111.14094/) (2021)
- [A Semi-supervised Learning Approach with Two Teachers to Improve Breakdown Identification in Dialogues](https://mlsanity.com/paper/2202.10948/) (2022)
- [Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data](https://mlsanity.com/paper/2206.09591/) (2022)
- [Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering](https://mlsanity.com/paper/2108.09689/) (2021)
- [Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction](https://mlsanity.com/paper/2208.08280/) (2022)
- [DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation](https://mlsanity.com/paper/2308.02753/) (2023)
- [Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer](https://mlsanity.com/paper/2109.03819/) (2021)
- [Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?](https://mlsanity.com/paper/2203.16926/) (2022)
- [Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction](https://mlsanity.com/paper/2302.14719/) (2023)
- [Depth F1: Improving Evaluation of Cross-Domain Text Classification by Measuring Semantic Generalizability](https://mlsanity.com/paper/2406.14695/) (2024)

## Abstract (arXiv)

We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations -- entropy minimization and self-ensemble bootstrapping -- to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.