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Conversational machine comprehension requires the understanding of theconversation history, such as previous question/answer pairs, the documentcontext, and the current question. To enable traditional, single-turn models toencode the history comprehensively, we introduce Flow, a mechanism that canincorporate intermediate representations generated during the process ofanswering previous questions, through an alternating parallel processingstructure. Compared to approaches that concatenate previous questions/answersas input, Flow integrates the latent semantics of the conversation history moredeeply. Our model, FlowQA, shows superior performance on two recently proposedconversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). Theeffectiveness of Flow also shows in other tasks. By reducing sequentialinstruction understanding to conversational machine comprehension, FlowQAoutperforms the best models on all three domains in SCONE, with +1.8% to +4.4%improvement in accuracy.

FlowQA: Grasping Flow in History for Conversational Machine Comprehension