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Multi-Domain Dialogue State Tracking based on State Graph
We investigate the problem of multi-domain Dialogue State Tracking (DST)...
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The SPPD System for Schema Guided Dialogue State Tracking Challenge
This paper introduces one of our group's work on the Dialog System Techn...
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A Sequence-to-Sequence Approach to Dialogue State Tracking
This paper is concerned with dialogue state tracking (DST) in a task-ori...
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When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation
Despite the multi-turn open-domain dialogue systems have attracted more ...
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Efficient Dialogue State Tracking by Selectively Overwriting Memory
Recent works in dialogue state tracking (DST) focus on an open vocabular...
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Learning Personas from Dialogue with Attentive Memory Networks
The ability to infer persona from dialogue can have applications in area...
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Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles
The ability to accurately track what happens during a conversation is es...
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Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we propose to utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can obtain new state-of-the-art performance of the open-vocabulary DST on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.
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