mlsanityData to 7 Oct 2026

QAConv: Question Answering on Informative Conversations

arXiv 2105.06912 ↗First posted 14 May 2021cs.CL cs.AI cs.IRcited by 17

Not on the map. Why?

Abstract (arXiv)

This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source. We focus on informative conversations, including business emails, panel discussions, and work channels. Unlike open-domain and task-oriented dialogues, these conversations are usually long, complex, asynchronous, and involve strong domain knowledge. In total, we collect 34,608 QA pairs from 10,259 selected conversations with both human-written and machine-generated questions. We use a question generator and a dialogue summarizer as auxiliary tools to collect and recommend questions. The dataset has two testing scenarios: chunk mode and full mode, depending on whether the grounded partial conversation is provided or retrieved. Experimental results show that state-of-the-art pretrained QA systems have limited zero-shot performance and tend to predict our questions as unanswerable. Our dataset provides a new training and evaluation testbed to facilitate QA on conversations research.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 35 of its 50 references that are in the map. First posted on arXiv 14 May 2021.

Builds on

35 of 50 references.

References · 35

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  5. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  6. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  7. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  8. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  9. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  10. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  11. 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
  12. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
23 more
  1. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  2. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 851
  3. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
  4. 2018CoQA: A Conversational Question Answering Challengecited by 674
  5. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 649
  6. 2019SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationcited by 530
  7. 2020UnifiedQA: Crossing Format Boundaries With a Single QA Systemcited by 475
  8. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  9. 2018QuAC : Question Answering in Contextcited by 379
  10. 2021QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarizationcited by 363
  11. 2018The Natural Language Decathlon: Multitask Learning as Question Answeringcited by 235
  12. 2018Constituency Parsing with a Self-Attentive Encodercited by 196
  13. 2019TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agentscited by 188
  14. 2020TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialoguecited by 185
  15. 2015The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systemscited by 172
  16. 2019DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehensioncited by 156
  17. 2015The Goldilocks Principle: Reading Children's Books with Explicit Memory Representationscited by 108
  18. 2021MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarizationcited by 104
  19. 2020Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structurecited by 60
  20. 2020DoQA -- Accessing Domain-Specific FAQs via Conversational QAcited by 42
  21. 2021Zero-shot Generalization in Dialog State Tracking through Generative Question Answeringcited by 33
  22. 2016Who did What: A Large-Scale Person-Centered Cloze Datasetcited by 24
  23. 2019Getting To Know You: User Attribute Extraction from Dialoguescited by 19

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 5; 2023: 3; 2024: 3; 2025: 3.

17 papers.

Sort:

By area:

  1. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 126
  2. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  3. 2022UnifiedQA-v2: Stronger Generalization via Broader Cross-Format Trainingcited by 57
  4. 2022InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuningcited by 39
  5. 2021MixQG: Neural Question Generation with Mixed Answer Typescited by 28
  6. 2022RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Questioncited by 17
  7. 2022Mediators: Conversational Agents Explaining NLP Model Behaviorcited by 7
  8. 2023Long-Tailed Question Answering in an Open Worldcited by 7
  9. 2024Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcriptscited by 6
  10. 2024A Little Human Data Goes A Long Waycited by 6
  11. 2025Measuring the Effect of Transcription Noise on Downstream Language Understanding Taskscited by 6
  12. 2023Teaching Smaller Language Models To Generalise To Unseen Compositional Questionscited by 2
5 more
  1. 2024Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Modelscited by 2
  2. 2021Taming the Beast: Learning to Control Neural Conversational Modelscited by 0
  3. 2023Contrastive Learning for Inference in Dialoguecited by 0
  4. 2025CXMArena: Unified Dataset to benchmark performance in realistic CXM Scenarioscited by 0
  5. 2025Faithfulness metric fusion: Improving the evaluation of LLM trustworthiness across domainscited by 0