mlsanityData to 14 Oct 2026

FlowQA: Grasping Flow in History for Conversational Machine Comprehension

arXiv 1810.06683 ↗First posted 6 Oct 2018cs.CL cs.AIcited by 33

Not on the map. Why?

Abstract (arXiv)

Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to approaches that concatenate previous questions/answers as input, Flow integrates the latent semantics of the conversation history more deeply. Our model, FlowQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of Flow also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FlowQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Oct 2018.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 8; 2022: 10; 2023: 10; 2024: 4; 2025: 1.

33 papers.

Sort:

By area:

  1. 2024Large Language Models: A Surveycited by 525
  2. 2023Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactionscited by 70
  3. 2022Neural Approaches to Conversational Information Retrievalcited by 42
  4. 2021Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answeringcited by 39
  5. 2022End-to-end Spoken Conversational Question Answering: Task, Dataset and Modelcited by 37
  6. 2021Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answeringcited by 27
  7. 2022Conversational Question Answering on Heterogeneous Sourcescited by 26
  8. 2021Structural Pre-training for Dialogue Comprehensioncited by 20
  9. 2022Saving Dense Retriever from Shortcut Dependency in Conversational Searchcited by 19
  10. 2023Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledgecited by 18
  11. 2022Generating Information-Seeking Conversations from Unlabeled Documentscited by 17
  12. 2021RoR: Read-over-Read for Long Document Machine Reading Comprehensioncited by 15
21 more
  1. 2021NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Datasetcited by 14
  2. 2021Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehensioncited by 13
  3. 2022On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Methodcited by 12
  4. 2022Open-Domain Conversational Question Answering with Historical Answerscited by 8
  5. 2021A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answeringcited by 7
  6. 2022Interactive Question Answering Systems: Literature Reviewcited by 7
  7. 2023CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generationcited by 6
  8. 2022LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-trainingcited by 5
  9. 2021Weakly-Supervised Open-Retrieval Conversational Question Answeringcited by 4
  10. 2023Learning to Select the Relevant History Turns in Conversational Question Answeringcited by 4
  11. 2024Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rightscited by 4
  12. 2022Does Entity Abstraction Help Generative Transformers Reason?cited by 3
  13. 2023Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answeringcited by 3
  14. 2023S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answeringcited by 3
  15. 2023Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurementcited by 2
  16. 2023Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learningcited by 2
  17. 2024Consistency Training by Synthetic Question Generation for Conversational Question Answeringcited by 2
  18. 2024Speech Technology Services for Oral History Researchcited by 2
  19. 2023Prompt Guided Copy Mechanism for Conversational Question Answeringcited by 1
  20. 2023Teach model to answer questions after comprehending the documentcited by 0
  21. 2025Building Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questionscited by 0