# Domain-agnostic Question-Answering with Adversarial Training

Source: [Domain-agnostic Question-Answering with Adversarial Training](<https://www.sota2.com/research/paper/68fa66941e209131cea11161>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:53:21.106Z

Authors: Seanie Lee, Donggyu Kim, Jangwon Park

Published: 2019-10-21T13:11:21.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.09342>)

[PDF](<https://arxiv.org/pdf/1910.09342.pdf>)