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show that the proposed framework achieves good performance in both offline evaluation metrics and in task success rate with human users. Moreover, this paper shows the flexibility of the proposed method by interleaving chatting capability with a slot-filling system for better out-of-domain recovery.
The models were trained on both real-user data from a bus information system and human-human chat data. Chattin you perform as a Streamray model, you will have complete control over who you interact with.
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This paper presents a practical and novel framework for building task-oriented dialog systems based on encoder-decoder models.