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Dialog state tracking (DST) is a core component in task-oriented dialogsystems. Existing approaches for DST mainly fall into one of two categories,namely, ontology-based and ontology-free methods. An ontology-based methodselects a value from a candidate-value list for each target slot, while anontology-free method extracts spans from dialog contexts. Recent workintroduced a BERT-based model to strike a balance between the two methods bypre-defining categorical and non-categorical slots. However, it is not clearenough which slots are better handled by either of the two slot types, and theway to use the pre-trained model has not been well investigated. In this paper,we propose a simple yet effective dual-strategy model for DST, by adapting asingle BERT-style reading comprehension model to jointly handle both thecategorical and non-categorical slots. Our experiments on the MultiWOZ datasetsshow that our method significantly outperforms the BERT-based counterpart,finding that the key is a deep interaction between the domain-slot and contextinformation. When evaluated on noisy (MultiWOZ 2.0) and cleaner (MultiWOZ 2.1)settings, our method performs competitively and robustly across the twodifferent settings. Our method sets the new state of the art in the noisysetting, while performing more robustly than the best model in the cleanersetting. We also conduct a comprehensive error analysis on the dataset,including the effects of the dual strategy for each slot, to facilitate futureresearch.

Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking