FlowQA: Grasping Flow in History for Conversational Machine Comprehension

@article{Huang2018FlowQAGF,
  title={FlowQA: Grasping Flow in History for Conversational Machine Comprehension},
  author={Hsin-Yuan Huang and Eunsol Choi and Wen-tau Yih},
  journal={CoRR},
  year={2018},
  volume={abs/1810.06683}
}
Conversational machine comprehension requires a deep understanding of the conversation history. 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 shallow approaches that concatenate previous questions/answers as input, FLOW integrates the latent semantics of… CONTINUE READING
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Key Quantitative Results

  • Our model, FLOWQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC).

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