# Adversarial Domain Adaptation for Machine Reading Comprehension

arXiv: https://arxiv.org/abs/1908.09209 · HTML: https://mlsanity.com/paper/1908.09209/

## Key facts

- Cited by 21 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 24 Aug 2019.

## Built on by (most cited first, 21 of 21)

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- [Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models](https://mlsanity.com/paper/2110.07736/) (2021)
- [On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.03154/) (2022)
- [Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation](https://mlsanity.com/paper/2210.02952/) (2022)
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- [Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases](https://mlsanity.com/paper/2302.00609/) (2023)
- [Synthetic Question Value Estimation for Domain Adaptation of Question Answering](https://mlsanity.com/paper/2203.08926/) (2022)
- [Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarization](https://mlsanity.com/paper/2403.19317/) (2024)
- [Topic Transferable Table Question Answering](https://mlsanity.com/paper/2109.07377/) (2021)
- [Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2310.16959/) (2023)
- [BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Task](https://mlsanity.com/paper/2202.13174/) (2022)
- [Source-Free Domain Adaptation for Question Answering with Masked Self-training](https://mlsanity.com/paper/2212.09563/) (2022)
- [Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management](https://mlsanity.com/paper/2104.01436/) (2021)
- [Towards More Robust Natural Language Understanding](https://mlsanity.com/paper/2112.02992/) (2021)
- [Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation](https://mlsanity.com/paper/2208.05217/) (2022)
- [Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization](https://mlsanity.com/paper/2106.16013/) (2021)
- [Robust Domain Adaptation for Machine Reading Comprehension](https://mlsanity.com/paper/2209.11615/) (2022)
- [Out-of-Distribution Generalization in Text Classification: Past, Present, and Future](https://mlsanity.com/paper/2305.14104/) (2023)
- [An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation](https://mlsanity.com/paper/2102.09786/) (2021)
- [ReMask: A Robust Information-Masking Approach for Domain Counterfactual Generation](https://mlsanity.com/paper/2305.02858/) (2023)
- [Unbiased Decisions Reduce Regret: Adversarial Domain Adaptation for the Bank Loan Problem](https://mlsanity.com/paper/2308.08051/) (2023)

## Abstract (arXiv)

In this paper, we focus on unsupervised domain adaptation for Machine Reading Comprehension (MRC), where the source domain has a large amount of labeled data, while only unlabeled passages are available in the target domain. To this end, we propose an Adversarial Domain Adaptation framework (AdaMRC), where (i) pseudo questions are first generated for unlabeled passages in the target domain, and then (ii) a domain classifier is incorporated into an MRC model to predict which domain a given passage-question pair comes from. The classifier and the passage-question encoder are jointly trained using adversarial learning to enforce domain-invariant representation learning. Comprehensive evaluations demonstrate that our approach (i) is generalizable to different MRC models and datasets, (ii) can be combined with pre-trained large-scale language models (such as ELMo and BERT), and (iii) can be extended to semi-supervised learning.