# A Robust Contrastive Alignment Method For Multi-Domain Text Classification

arXiv: https://arxiv.org/abs/2204.12125 · HTML: https://mlsanity.com/paper/2204.12125/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Adversarial Multi-task Learning for Text Classification](https://mlsanity.com/paper/1704.05742/) (2017)
- [Multinomial Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/1802.05694/) (2018)
- [Conditional Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.10176/) (2021)
- [Dual Adversarial Co-Learning for Multi-Domain Text Classification](https://mlsanity.com/paper/1909.08203/) (2019)
- [Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling](https://mlsanity.com/paper/2110.03572/) (2021)
- [Mixup Regularized Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.00467/) (2021)

## Built on by (most cited first, 6 of 6)

- [Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task](https://mlsanity.com/paper/2310.06504/) (2023)
- [Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA](https://mlsanity.com/paper/2308.14436/) (2023)
- [A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER](https://mlsanity.com/paper/2308.14533/) (2023)
- [Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations](https://mlsanity.com/paper/2310.03518/) (2023)
- [Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement](https://mlsanity.com/paper/2309.09799/) (2023)
- [Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task](https://mlsanity.com/paper/2402.14494/) (2024)

## Abstract (arXiv)

Multi-domain text classification can automatically classify texts in various scenarios. Due to the diversity of human languages, texts with the same label in different domains may differ greatly, which brings challenges to the multi-domain text classification. Current advanced methods use the private-shared paradigm, capturing domain-shared features by a shared encoder, and training a private encoder for each domain to extract domain-specific features. However, in realistic scenarios, these methods suffer from inefficiency as new domains are constantly emerging. In this paper, we propose a robust contrastive alignment method to align text classification features of various domains in the same feature space by supervised contrastive learning. By this means, we only need two universal feature extractors to achieve multi-domain text classification. Extensive experimental results show that our method performs on par with or sometimes better than the state-of-the-art method, which uses the complex multi-classifier in a private-shared framework.