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- W2972826691 abstract "Dialogue state tracking is an important component in task-oriented dialogue systems to identify users' goals and requests as a dialogue proceeds. However, as most previous models are dependent on dialogue slots, the model complexity soars when the number of slots increases. In this paper, we put forward a slot-independent neural model (SIM) to track dialogue states while keeping the model complexity invariant to the number of dialogue slots. The model utilizes attention mechanisms between user utterance and system actions. SIM achieves state-of-the-art results on WoZ and DSTC2 tasks, with only 20% of the model size of previous models." @default.
- W2972826691 created "2019-09-19" @default.
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- W2972826691 date "2019-09-25" @default.
- W2972826691 modified "2023-09-26" @default.
- W2972826691 title "SIM: A Slot-Independent Neural Model for Dialogue State Tracking" @default.
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- W2972826691 doi "https://doi.org/10.48550/arxiv.1909.11833" @default.
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