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DoQA -- Accessing Domain-Specific FAQs via Conversational QA

arXiv 2005.01328 ↗First posted 4 May 2020cs.CLcited by 42

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Abstract (arXiv)

The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites. We present DoQA, a dataset with 2,437 dialogues and 10,917 QA pairs. The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing. Compared to previous work, DoQA comprises well-defined information needs, leading to more coherent and natural conversations with less factoid questions and is multi-domain. In addition, we introduce a more realistic information retrieval(IR) scenario where the system needs to find the answer in any of the FAQ documents. The results of an existing, strong, system show that, thanks to transfer learning from a Wikipedia QA dataset and fine tuning on a single FAQ domain, it is possible to build high quality conversational QA systems for FAQs without in-domain training data. The good results carry over into the more challenging IR scenario. In both cases, there is still ample room for improvement, as indicated by the higher human upperbound.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 May 2020.

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Citing papers per year: 2021: 8; 2022: 11; 2023: 8; 2024: 8; 2025: 5; 2026: 2.

42 papers.

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  1. 2023Benchmarking Foundation Models with Language-Model-as-an-Examinercited by 172
  2. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  3. 2024ChatQA: Surpassing GPT-4 on Conversational QA and RAGcited by 90
  4. 2024Instruction Pre-Training: Language Models are Supervised Multitask Learnerscited by 63
  5. 2022Dialog Inpainting: Turning Documents into Dialogscited by 56
  6. 2021MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documentscited by 53
  7. 2022HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Datacited by 28
  8. 2022INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactionscited by 27
  9. 2021Ditch the Gold Standard: Re-evaluating Conversational Question Answeringcited by 19
  10. 2021QAConv: Question Answering on Informative Conversationscited by 17
  11. 2022Generating Information-Seeking Conversations from Unlabeled Documentscited by 17
  12. 2022On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Methodcited by 12
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  1. 2021DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encodercited by 11
  2. 2021DG2: Data Augmentation Through Document Grounded Dialogue Generationcited by 10
  3. 2023textitDial BeInfo for Faithfulness: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuningcited by 9
  4. 2024Structured Chain-of-Thought Prompting for Few-Shot Generation of Content-Grounded QA Conversationscited by 9
  5. 2025WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generationcited by 9
  6. 2022Can Question Rewriting Help Conversational Question Answering?cited by 8
  7. 2022ELQA: A Corpus of Metalinguistic Questions and Answers about Englishcited by 8
  8. 2022Task2Dial: A Novel Task and Dataset for Commonsense enhanced Task-based Dialogue Grounded in Documentscited by 7
  9. 2023Improved Instruction Ordering in Recipe-Grounded Conversationcited by 7
  10. 2024Beyond Static Evaluation: A Dynamic Approach to Assessing AI Assistants' API Invocation Capabilitiescited by 6
  11. 2024Efficient Data Generation for Source-grounded Information-seeking Dialogs: A Use Case for Meeting Transcriptscited by 6
  12. 2024Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applicationscited by 6
  13. 2021CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue Systemcited by 5
  14. 2022Conversational QA Dataset Generation with Answer Revisioncited by 5
  15. 2023NewsDialogues: Towards Proactive News Grounded Conversationcited by 5
  16. 2023PCoQA: Persian Conversational Question Answering Datasetcited by 5
  17. 2023Causal Document-Grounded Dialogue Pre-trainingcited by 4
  18. 2021Variational Learning for Unsupervised Knowledge Grounded Dialogscited by 3
  19. 2022Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstrationcited by 3
  20. 2023A System for Human-AI collaboration for Online Customer Supportcited by 3
  21. 2024Learning from Implicit User Feedback, Emotions and Demographic Information in Task-Oriented and Document-Grounded Dialoguescited by 3
  22. 2022CookDial: A dataset for task-oriented dialogs grounded in procedural documentscited by 2
  23. 2025On Synthesizing Data for Context Attribution in Question Answeringcited by 2
  24. 2025KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialoguescited by 2
  25. 2023Orca: A Few-shot Benchmark for Chinese Conversational Machine Reading Comprehensioncited by 1
  26. 2025Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximationcited by 1
  27. 2024Structured List-Grounded Question Answeringcited by 0
  28. 2025Building Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questionscited by 0
  29. 2026Comprehensive Comparison of RAG Methods Across Multi-Domain Conversational QAcited by 0
  30. 2026Evaluating LLMs' Effectiveness on Real-World Consumer Device Repair Questionscited by 0