# Learning a Cost-Effective Annotation Policy for Question Answering

arXiv: https://arxiv.org/abs/2010.03476 · HTML: https://mlsanity.com/paper/2010.03476/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Learning with Different Amounts of Annotation: From Zero to Many Labels](https://mlsanity.com/paper/2109.04408/) (2021)
- [Simulating Bandit Learning from User Feedback for Extractive Question Answering](https://mlsanity.com/paper/2203.10079/) (2022)
- [ORANSight-2.0: Foundational LLMs for O-RAN](https://mlsanity.com/paper/2503.05200/) (2025)
- [mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans](https://mlsanity.com/paper/2406.04215/) (2024)
- [Capturing Label Distribution: A Case Study in NLI](https://mlsanity.com/paper/2102.06859/) (2021)
- [Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering](https://mlsanity.com/paper/2306.06779/) (2023)
- [Combining Data Generation and Active Learning for Low-Resource Question Answering](https://mlsanity.com/paper/2211.14880/) (2022)
- [QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation](https://mlsanity.com/paper/2210.10861/) (2022)
- [DomainInv: Domain Invariant Fine Tuning and Adversarial Label Correction For QA Domain Adaptation](https://mlsanity.com/paper/2305.05589/) (2023)

## Abstract (arXiv)

State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems is challenging. As a remedy, we propose a novel framework for annotating QA datasets that entails learning a cost-effective annotation policy and a semi-supervised annotation scheme. The latter reduces the human effort: it leverages the underlying QA system to suggest potential candidate annotations. Human annotators then simply provide binary feedback on these candidates. Our system is designed such that past annotations continuously improve the future performance and thus overall annotation cost. To the best of our knowledge, this is the first paper to address the problem of annotating questions with minimal annotation cost. We compare our framework against traditional manual annotations in an extensive set of experiments. We find that our approach can reduce up to 21.1% of the annotation cost.