# Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networks

arXiv: https://arxiv.org/abs/1911.02655 · HTML: https://mlsanity.com/paper/1911.02655/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [Contrastive Domain Adaptation for Question Answering using Limited Text Corpora](https://mlsanity.com/paper/2108.13854/) (2021)
- [Synthetic Question Value Estimation for Domain Adaptation of Question Answering](https://mlsanity.com/paper/2203.08926/) (2022)
- [Domain Adaptation for Question Answering via Question Classification](https://mlsanity.com/paper/2209.04998/) (2022)
- [On the Universality of Deep Contextual Language Models](https://mlsanity.com/paper/2109.07140/) (2021)
- [Understanding Domain Learning in Language Models Through Subpopulation Analysis](https://mlsanity.com/paper/2210.12553/) (2022)
- [Task Transfer and Domain Adaptation for Zero-Shot Question Answering](https://mlsanity.com/paper/2206.06705/) (2022)
- [Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints](https://mlsanity.com/paper/2401.09168/) (2024)

## Abstract (arXiv)

This paper explores domain adaptation for enabling question answering (QA) systems to answer questions posed against documents in new specialized domains. Current QA systems using deep neural network (DNN) technology have proven effective for answering general purpose factoid-style questions. However, current general purpose DNN models tend to be ineffective for use in new specialized domains. This paper explores the effectiveness of transfer learning techniques for this problem. In experiments on question answering in the automobile manual domain we demonstrate that standard DNN transfer learning techniques work surprisingly well in adapting DNN models to a new domain using limited amounts of annotated training data in the new domain.