mlsanityData to 14 Oct 2026

Synthetic Question Value Estimation for Domain Adaptation of Question Answering

arXiv 2203.08926 ↗First posted 16 Mar 2022cs.CLcited by 12

Not on the map. Why?

Abstract (arXiv)

Synthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models. Since synthetic questions are often noisy in practice, existing work adapts scores from a pretrained QA (or QG) model as criteria to select high-quality questions. However, these scores do not directly serve the ultimate goal of improving QA performance on the target domain. In this paper, we introduce a novel idea of training a question value estimator (QVE) that directly estimates the usefulness of synthetic questions for improving the target-domain QA performance. By conducting comprehensive experiments, we show that the synthetic questions selected by QVE can help achieve better target-domain QA performance, in comparison with existing techniques. We additionally show that by using such questions and only around 15% of the human annotations on the target domain, we can achieve comparable performance to the fully-supervised baselines.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 37 of its 47 references that are in the map. First posted on arXiv 16 Mar 2022.

Builds on

37 of 47 references.

References · 37

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  4. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  5. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  6. 2016Training Deep Nets with Sublinear Memory Costcited by 848
  7. 2019Unified Language Model Pre-training for Natural Language Understanding and Generationcited by 670
  8. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 629
  9. 2016NewsQA: A Machine Comprehension Datasetcited by 331
  10. 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 281
  11. 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
  12. 2019MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehensioncited by 184
25 more
  1. 2019Synthetic QA Corpora Generation with Roundtrip Consistencycited by 143
  2. 2019Data Valuation using Reinforcement Learningcited by 116
  3. 2020Training Question Answering Models From Synthetic Datacited by 95
  4. 2017Neural Question Generation from Text: A Preliminary Studycited by 78
  5. 2020End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systemscited by 65
  6. 2017Learning to select data for transfer learning with Bayesian Optimizationcited by 64
  7. 2019Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answeringcited by 63
  8. 2019Unsupervised Question Answering by Cloze Translationcited by 59
  9. 2018Harvesting Paragraph-Level Question-Answer Pairs from Wikipediacited by 49
  10. 2020Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpuscited by 40
  11. 2020Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answeringcited by 39
  12. 2020Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEscited by 31
  13. 2017Neural Domain Adaptation for Biomedical Question Answeringcited by 30
  14. 2017Supervised and Unsupervised Transfer Learning for Question Answeringcited by 24
  15. 2018Simple and Effective Semi-Supervised Question Answeringcited by 22
  16. 2019Adversarial Domain Adaptation for Machine Reading Comprehensioncited by 21
  17. 2020Harvesting and Refining Question-Answer Pairs for Unsupervised QAcited by 20
  18. 2021Contrastive Domain Adaptation for Question Answering using Limited Text Corporacited by 20
  19. 2019Capturing Greater Context for Question Generationcited by 19
  20. 2019Unsupervised Domain Adaptation on Reading Comprehensioncited by 14
  21. 2019Let's Ask Again: Refine Network for Automatic Question Generationcited by 13
  22. 2017Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehensioncited by 11
  23. 2019Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Networkscited by 7
  24. 2018Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matchingcited by 4
  25. 2020Improved Synthetic Training for Reading Comprehensioncited by 4

Built on by

Cited by year

Citing papers per year: 2022: 5; 2023: 4; 2024: 3.

12 papers.

  1. 2022ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examplescited by 29
  2. 2022Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Surveycited by 14
  3. 2022Domain Adaptation for Question Answering via Question Classificationcited by 7
  4. 2023Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generationcited by 6
  5. 2023Learning to Generalize for Cross-domain QAcited by 6
  6. 2023PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Developmentcited by 4
  7. 2022Source-Free Domain Adaptation for Question Answering with Masked Self-trainingcited by 3
  8. 2022QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptationcited by 2
  9. 2024Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generationcited by 2
  10. 2024Birdie: Advancing State Space Models with Reward-Driven Objectives and Curriculacited by 1
  11. 2023DomainInv: Domain Invariant Fine Tuning and Adversarial Label Correction For QA Domain Adaptationcited by 0
  12. 2024Domain Adaptation of Multilingual Semantic Search -- Literature Reviewcited by 0