# Cross-Domain Sentiment Classification with In-Domain Contrastive Learning

arXiv: https://arxiv.org/abs/2012.02943 · HTML: https://mlsanity.com/paper/2012.02943/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification](https://mlsanity.com/paper/2205.08772/) (2022)
- [Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation](https://mlsanity.com/paper/2305.05827/) (2023)
- [Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization](https://mlsanity.com/paper/2108.11992/) (2021)

## Abstract (arXiv)

Contrastive learning (CL) has been successful as a powerful representation learning method. In this paper, we propose a contrastive learning framework for cross-domain sentiment classification. We aim to induce domain invariant optimal classifiers rather than distribution matching. To this end, we introduce in-domain contrastive learning and entropy minimization. Also, we find through ablation studies that these two techniques behaviour differently in case of large label distribution shift and conclude that the best practice is to choose one of them adaptively according to label distribution shift. The new state-of-the-art results our model achieves on standard benchmarks show the efficacy of the proposed method.