# Domain-agnostic Question-Answering with Adversarial Training

arXiv: https://arxiv.org/abs/1910.09342 · HTML: https://mlsanity.com/paper/1910.09342/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 22 of 22)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.03154/) (2022)
- [Contrastive Domain Adaptation for Question Answering using Limited Text Corpora](https://mlsanity.com/paper/2108.13854/) (2021)
- [Simulating Bandit Learning from User Feedback for Extractive Question Answering](https://mlsanity.com/paper/2203.10079/) (2022)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [Learning to Perturb Word Embeddings for Out-of-distribution QA](https://mlsanity.com/paper/2105.02692/) (2021)
- [Defending Against Disinformation Attacks in Open-Domain Question Answering](https://mlsanity.com/paper/2212.10002/) (2022)
- [Topic Transferable Table Question Answering](https://mlsanity.com/paper/2109.07377/) (2021)
- [Domain Adaptation for Question Answering via Question Classification](https://mlsanity.com/paper/2209.04998/) (2022)
- [Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering](https://mlsanity.com/paper/2205.07257/) (2022)
- [Combining Data Generation and Active Learning for Low-Resource Question Answering](https://mlsanity.com/paper/2211.14880/) (2022)
- [Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup](https://mlsanity.com/paper/2210.03250/) (2022)
- [Using Large Language Models for education managements in Vietnamese with low resources](https://mlsanity.com/paper/2501.15022/) (2025)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [Model Agnostic Answer Reranking System for Adversarial Question Answering](https://mlsanity.com/paper/2102.03016/) (2021)
- [QAGAN: Adversarial Approach To Learning Domain Invariant Language Features](https://mlsanity.com/paper/2206.12388/) (2022)
- [PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN](https://mlsanity.com/paper/2207.01762/) (2022)
- [Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation](https://mlsanity.com/paper/2208.05217/) (2022)
- [QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation](https://mlsanity.com/paper/2210.10861/) (2022)
- [Out-of-Distribution Generalization in Text Classification: Past, Present, and Future](https://mlsanity.com/paper/2305.14104/) (2023)
- [DomainInv: Domain Invariant Fine Tuning and Adversarial Label Correction For QA Domain Adaptation](https://mlsanity.com/paper/2305.05589/) (2023)
- [Domain Adaptation of Multilingual Semantic Search -- Literature Review](https://mlsanity.com/paper/2402.02932/) (2024)

## Abstract (arXiv)

Adapting models to new domain without finetuning is a challenging problem in deep learning. In this paper, we utilize an adversarial training framework for domain generalization in Question Answering (QA) task. Our model consists of a conventional QA model and a discriminator. The training is performed in the adversarial manner, where the two models constantly compete, so that QA model can learn domain-invariant features. We apply this approach in MRQA Shared Task 2019 and show better performance compared to the baseline model.