# FlowQA: Grasping Flow in History for Conversational Machine Comprehension

arXiv: https://arxiv.org/abs/1810.06683 · HTML: https://mlsanity.com/paper/1810.06683/

## Key facts

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

## Built on by (most cited first, 30 of 33)

- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactions](https://mlsanity.com/paper/2312.02913/) (2023)
- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering](https://mlsanity.com/paper/2109.03381/) (2021)
- [End-to-end Spoken Conversational Question Answering: Task, Dataset and Model](https://mlsanity.com/paper/2204.14272/) (2022)
- [Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering](https://mlsanity.com/paper/2106.11575/) (2021)
- [Conversational Question Answering on Heterogeneous Sources](https://mlsanity.com/paper/2204.11677/) (2022)
- [Structural Pre-training for Dialogue Comprehension](https://mlsanity.com/paper/2105.10956/) (2021)
- [Saving Dense Retriever from Shortcut Dependency in Conversational Search](https://mlsanity.com/paper/2202.07280/) (2022)
- [Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge](https://mlsanity.com/paper/2301.01067/) (2023)
- [Generating Information-Seeking Conversations from Unlabeled Documents](https://mlsanity.com/paper/2205.12609/) (2022)
- [RoR: Read-over-Read for Long Document Machine Reading Comprehension](https://mlsanity.com/paper/2109.04780/) (2021)
- [NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset](https://mlsanity.com/paper/2109.10604/) (2021)
- [Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2109.04066/) (2021)
- [On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method](https://mlsanity.com/paper/2206.14796/) (2022)
- [Open-Domain Conversational Question Answering with Historical Answers](https://mlsanity.com/paper/2211.09401/) (2022)
- [A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering](https://mlsanity.com/paper/2104.08443/) (2021)
- [Interactive Question Answering Systems: Literature Review](https://mlsanity.com/paper/2209.01621/) (2022)
- [CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation](https://mlsanity.com/paper/2309.06748/) (2023)
- [LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training](https://mlsanity.com/paper/2201.08081/) (2022)
- [Weakly-Supervised Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2103.02537/) (2021)
- [Learning to Select the Relevant History Turns in Conversational Question Answering](https://mlsanity.com/paper/2308.02294/) (2023)
- [Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights](https://mlsanity.com/paper/2403.12678/) (2024)
- [Does Entity Abstraction Help Generative Transformers Reason?](https://mlsanity.com/paper/2201.01787/) (2022)
- [Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering](https://mlsanity.com/paper/2304.07125/) (2023)
- [S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering](https://mlsanity.com/paper/2312.16511/) (2023)
- [Realistic Conversational Question Answering with Answer Selection based on Calibrated Confidence and Uncertainty Measurement](https://mlsanity.com/paper/2302.05137/) (2023)
- [Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning](https://mlsanity.com/paper/2306.04293/) (2023)
- [Consistency Training by Synthetic Question Generation for Conversational Question Answering](https://mlsanity.com/paper/2404.11109/) (2024)
- [Speech Technology Services for Oral History Research](https://mlsanity.com/paper/2405.02333/) (2024)

## 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.