# Neural Structural Correspondence Learning for Domain Adaptation

arXiv: https://arxiv.org/abs/1610.01588 · HTML: https://mlsanity.com/paper/1610.01588/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2016.

## Built on by (most cited first, 19 of 19)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Multi-Source Domain Adaptation with Mixture of Experts](https://mlsanity.com/paper/1809.02256/) (2018)
- [PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains](https://mlsanity.com/paper/2102.12206/) (2021)
- [Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations](https://mlsanity.com/paper/2204.02683/) (2022)
- [DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation](https://mlsanity.com/paper/2202.12350/) (2022)
- [UDALM: Unsupervised Domain Adaptation through Language Modeling](https://mlsanity.com/paper/2104.07078/) (2021)
- [Example-based Hypernetworks for Out-of-Distribution Generalization](https://mlsanity.com/paper/2203.14276/) (2022)
- [Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases](https://mlsanity.com/paper/2302.00609/) (2023)
- [DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extraction](https://mlsanity.com/paper/2109.00571/) (2021)
- [Measuring the Robustness of NLP Models to Domain Shifts](https://mlsanity.com/paper/2306.00168/) (2023)
- [Model Compression for Domain Adaptation through Causal Effect Estimation](https://mlsanity.com/paper/2101.07086/) (2021)
- [Explainable Misinformation Detection Across Multiple Social Media Platforms](https://mlsanity.com/paper/2203.11724/) (2022)
- [X2Parser: Cross-Lingual and Cross-Domain Framework for Task-Oriented Compositional Semantic Parsing](https://mlsanity.com/paper/2106.03777/) (2021)
- [Domain Confused Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.04564/) (2022)
- [Understanding Domain Learning in Language Models Through Subpopulation Analysis](https://mlsanity.com/paper/2210.12553/) (2022)
- [Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis](https://mlsanity.com/paper/2402.14536/) (2024)
- [A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2201.12549/) (2022)
- [ReMask: A Robust Information-Masking Approach for Domain Counterfactual Generation](https://mlsanity.com/paper/2305.02858/) (2023)
- [Multi-modal Domain Adaptation for REG via Relation Transfer](https://mlsanity.com/paper/2309.13247/) (2023)

## Abstract (arXiv)

Domain adaptation, adapting models from domains rich in labeled training data to domains poor in such data, is a fundamental NLP challenge. We introduce a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence learning (SCL, (Blitzer et al., 2006)) and autoencoder neural networks. Particularly, our model is a three-layer neural network that learns to encode the nonpivot features of an input example into a low-dimensional representation, so that the existence of pivot features (features that are prominent in both domains and convey useful information for the NLP task) in the example can be decoded from that representation. The low-dimensional representation is then employed in a learning algorithm for the task. Moreover, we show how to inject pre-trained word embeddings into our model in order to improve generalization across examples with similar pivot features. On the task of cross-domain product sentiment classification (Blitzer et al., 2007), consisting of 12 domain pairs, our model outperforms both the SCL and the marginalized stacked denoising autoencoder (MSDA, (Chen et al., 2012)) methods by 3.77% and 2.17% respectively, on average across domain pairs.