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• W2952230306 abstract "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." @default.
• W2952230306 created "2019-06-27" @default.
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• W2952230306 date "2018-10-06" @default.
• W2952230306 modified "2023-09-25" @default.
• W2952230306 title "FlowQA: Grasping Flow in History for Conversational Machine Comprehension" @default.
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• W2952230306 doi "https://doi.org/10.48550/arxiv.1810.06683" @default.
• W2952230306 hasPublicationYear "2018" @default.
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