# Multi-task Domain Adaptation for Sequence Tagging

arXiv: https://arxiv.org/abs/1608.02689 · HTML: https://mlsanity.com/paper/1608.02689/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2016.

## Built on by (most cited first, 8 of 8)

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- [Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields](https://mlsanity.com/paper/2404.09383/) (2024)
- [Boosting Entity Mention Detection for Targetted Twitter Streams with Global Contextual Embeddings](https://mlsanity.com/paper/2201.11885/) (2022)
- [Elastic Multi-Gradient Descent for Parallel Continual Learning](https://mlsanity.com/paper/2401.01054/) (2024)
- [Cross-Register Projection for Headline Part of Speech Tagging](https://mlsanity.com/paper/2109.07483/) (2021)
- [Sentence Specificity Scores for Collaborative Technical Documentation: A Domain-Transfer Study](https://mlsanity.com/paper/2610.01046/) (2026)

## Abstract (arXiv)

Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains. Traditional domain adaptation only considers adapting for one task. In this paper, we explore multi-task representation learning under the domain adaptation scenario. We propose a neural network framework that supports domain adaptation for multiple tasks simultaneously, and learns shared representations that better generalize for domain adaptation. We apply the proposed framework to domain adaptation for sequence tagging problems considering two tasks: Chinese word segmentation and named entity recognition. Experiments show that multi-task domain adaptation works better than disjoint domain adaptation for each task, and achieves the state-of-the-art results for both tasks in the social media domain.