Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking (2022.acl-long)
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| Challenge: | Existing approaches to model the relations between domains and slots fail to address these issues and can be generalized to unseen domains. |
| Approach: | They propose a Dynamic Schema Graph Fusion Network which generates a dynamic schema graph to explicitly fuse prior slot-domain membership relations and dialogue-aware dynamic slot relations. |
| Outcome: | The proposed model outperforms existing methods on benchmark datasets showing that it can extract users' goals or intentions as dialogue states and keep them updated over the whole dialogue. |
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| Challenge: | Existing methods to track dialogue state are lacking in multi-domain scenarios. |
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| Challenge: | Recent work has focused on deep neural models for task-oriented dialogue systems . however, the neural models require a large dataset for training and a new dataset to be trained on another domain. |
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| Challenge: | Existing schema-guided dialogue state tracking models do not account for schema variations and are not generalized to unseen services. |
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A Sequence-to-Sequence Approach to Dialogue State Tracking (2021.acl-long)
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| Challenge: | Existing methods for dialogue state tracking are still challenging, but they are improving . a new approach to dialogue state monitoring is proposed, called Seq2Seq-DU . |
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Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks (2021.emnlp-main)
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Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, Jun Zhao
| Challenge: | Existing offline DST models require a fixed dataset to train . Existing domain-lifelong learning methods are impractical in real-world applications . |
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| Challenge: | Existing methods for dialogue state tracking ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots. |
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Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation (D19-1)
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| Challenge: | Existing approaches to dialogue state tracking rely on pre-defined ontologies . however, these methods suffer from computational complexity that increases proportionally to the number of pre-determined slots. |
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