mlsanityData to 14 Oct 2026

A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC

arXiv 1809.10735 ↗First posted 27 Sep 2018cs.CLcited by 22

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Abstract (arXiv)

We compare three new datasets for question answering: SQuAD 2.0, QuAC, and CoQA, along several of their new features: (1) unanswerable questions, (2) multi-turn interactions, and (3) abstractive answers. We show that the datasets provide complementary coverage of the first two aspects, but weak coverage of the third. Because of the datasets' structural similarity, a single extractive model can be easily adapted to any of the datasets and we show improved baseline results on both SQuAD 2.0 and CoQA. Despite the similarity, models trained on one dataset are ineffective on another dataset, but we find moderate performance improvement through pretraining. To encourage cross-evaluation, we release code for conversion between datasets at https://github.com/my89/co-squac .

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Sep 2018.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 4; 2024: 2; 2025: 5; 2026: 1.

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  1. 2022DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answeringcited by 96
  2. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  3. 2023Context-faithful Prompting for Large Language Modelscited by 80
  4. 2022An Empirical Survey on Long Document Summarization: Datasets, Models and Metricscited by 74
  5. 2022Neural Approaches to Conversational Information Retrievalcited by 42
  6. 2021Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answeringcited by 41
  7. 2023Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Reviewcited by 39
  8. 2024A Survey of Recent Backdoor Attacks and Defenses in Large Language Modelscited by 36
  9. 2021Conversational Question Answering: A Surveycited by 31
  10. 2021How well do you know your summarization datasets?cited by 16
  11. 2022Learning Disentangled Representations of Negation and Uncertaintycited by 13
  12. 2023Learning to Select the Relevant History Turns in Conversational Question Answeringcited by 4
10 more
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  2. 2025FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Modelscited by 1
  3. 2025A Survey of the State-of-the-Art in Conversational Question Answering Systemscited by 1
  4. 2025Explainable Semantic Text Relations: A Question-Answering Framework for Comparing Document Contentcited by 1
  5. 2025SEAL: Self-Evolving Agentic Learning for Conversational Question Answering over Knowledge Graphscited by 1
  6. 2026TuneAhead: Predicting Fine-tuning Performance Before Full Training Beginscited by 1
  7. 2021How to Build Robust FAQ Chatbot with Controllable Question Generator?cited by 0
  8. 2023Combining EEG and NLP Features for Predicting Students' Lecture Comprehension using Ensemble Classificationcited by 0
  9. 2024Answerability in Retrieval-Augmented Open-Domain Question Answeringcited by 0
  10. 2025From the Rock Floor to the Cloud: A Systematic Survey of State-of-the-Art NLP in Battery Life Cyclecited by 0