mlsanityData to 7 Oct 2026

FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension

arXiv 1908.05117 ↗First posted 14 Aug 2019cs.CL cs.AIcited by 19

Not on the map. Why?

Abstract (arXiv)

Conversational machine comprehension requires deep understanding of the dialogue flow, and the prior work proposed FlowQA to implicitly model the context representations in reasoning for better understanding. This paper proposes to explicitly model the information gain through dialogue reasoning in order to allow the model to focus on more informative cues. The proposed model achieves state-of-the-art performance in a conversational QA dataset QuAC and sequential instruction understanding dataset SCONE, which shows the effectiveness of the proposed mechanism and demonstrates its capability of generalization to different QA models and tasks.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2019.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 7; 2022: 6; 2023: 4; 2024: 0; 2025: 2.

19 papers.

Sort:

By area:

  1. 2023Let the LLMs Talk: Simulating Human-to-Human Conversational QA via Zero-Shot LLM-to-LLM Interactionscited by 70
  2. 2022Neural Approaches to Conversational Information Retrievalcited by 41
  3. 2021Conversational Question Answering: A Surveycited by 31
  4. 2021Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answeringcited by 27
  5. 2021A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLPcited by 25
  6. 2021ChainCQG: Flow-Aware Conversational Question Generationcited by 19
  7. 2021RoR: Read-over-Read for Long Document Machine Reading Comprehensioncited by 15
  8. 2021BERT-CoQAC: BERT-based Conversational Question Answering in Contextcited by 13
  9. 2022On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Methodcited by 12
  10. 2022Can Question Rewriting Help Conversational Question Answering?cited by 8
  11. 2022Open-Domain Conversational Question Answering with Historical Answerscited by 8
  12. 2023CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generationcited by 6
7 more
  1. 2022LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-trainingcited by 5
  2. 2021Weakly-Supervised Open-Retrieval Conversational Question Answeringcited by 4
  3. 2023Learning to Select the Relevant History Turns in Conversational Question Answeringcited by 4
  4. 2023Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answeringcited by 3
  5. 2022IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selectioncited by 2
  6. 2025Flexible-weighted Chamfer Distance: Enhanced Objective Function for Point Cloud Completioncited by 2
  7. 2025Building Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questionscited by 0