They propose neural models for dialogue state tracking, making a binary decision for each possible slot-value pair, based on the latest context from the user and the system. The context utterances and the slot-value option are encoded into vectors, either by summing word representations or using a convnet. These vectors are then further combined to produce a binary output. The systems are evaluated on two dialogue datasets and show improvement over baselines that use hand-constructed lexicons.

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