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• W2949446780 abstract "Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus." @default.
• W2949446780 created "2019-06-27" @default.
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• W2949446780 date "2018-06-24" @default.
• W2949446780 modified "2023-09-27" @default.
• W2949446780 title "Modeling Multi-turn Conversation with Deep Utterance Aggregation" @default.
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• W2949446780 hasPublicationYear "2018" @default.
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