# Unsupervised Domain Adaptation on Reading Comprehension

arXiv: https://arxiv.org/abs/1911.06137 · HTML: https://mlsanity.com/paper/1911.06137/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [Synthetic Question Value Estimation for Domain Adaptation of Question Answering](https://mlsanity.com/paper/2203.08926/) (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)
- [Learning to Generalize for Cross-domain QA](https://mlsanity.com/paper/2305.08208/) (2023)
- [Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts](https://mlsanity.com/paper/2204.04775/) (2022)
- [Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision](https://mlsanity.com/paper/2107.08582/) (2021)
- [Source-Free Domain Adaptation for Question Answering with Masked Self-training](https://mlsanity.com/paper/2212.09563/) (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)
- [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)
- [CTTA-T: Continual Test-Time Adaptation for Text Understanding via Teacher-Student with a Domain-aware and Generalized Teacher](https://mlsanity.com/paper/2512.18321/) (2025)
- [An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation](https://mlsanity.com/paper/2102.09786/) (2021)
- [DomainInv: Domain Invariant Fine Tuning and Adversarial Label Correction For QA Domain Adaptation](https://mlsanity.com/paper/2305.05589/) (2023)

## Abstract (arXiv)

Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.