| Challenge: | Existing approaches to scalability of dialogue belief tracking are dependent on the ontology of the dialogue . current approaches are not scalable to multi-domain dialogues because of the effort required to define a semantic dictionary for each domain. |
| Approach: | They propose a model that utilizes semantic similarity between dialogue utterances and ontology terms to allow information to be shared across domains. |
| Outcome: | The proposed model outperforms state-of-the-art models in multi-domain dialogue tracking tasks while maintaining high quality. |
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Carel van Niekerk, Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Milica Gasic
| Challenge: | Current models for dialogue state tracking only achieve 55% accuracy . however, they lack in performance compared to belief trackers and do not produce well calibrated distributions. |
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| Outcome: | The proposed model outperforms existing models in terms of accuracy and accuracy. |
Scaling Multi-Domain Dialogue State Tracking via Query Reformulation (N19-2)
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| Challenge: | Using a pointer-generator network, we model the reference resolution task as a dialogue context-aware user query reformulation task. |
| Approach: | They propose a pointer-generator network and a novel multi-task learning setup to model dialogue state tracking and referring expression resolution tasks using a dialogue context-aware user query reformulation task. |
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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. |
| Approach: | They propose a model that generates a sequence of belief states without the pre-defined ontology list. |
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Dialogue State Tracking with Incremental Reasoning (2021.tacl-1)
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| Challenge: | Empirical results show that our method outperforms the state-of-the-art methods in terms of joint belief accuracy. |
| Approach: | They propose to track dialogue states gradually with reasoning over dialogue turns using the back-end data. |
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Towards Universal Dialogue State Tracking (D18-1)
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| Challenge: | Existing approaches to dialogue state tracking are difficult to scale to large dialogue domains. |
| Approach: | They propose a universal dialogue state tracker that is independent of the number of values and shares parameters across all slots. |
| Outcome: | The proposed system significantly outperforms state-of-the-art approaches on two datasets. |
Domain-specific Attention with Distributional Signatures for Multi-Domain End-to-end Task-Oriented Dialogue (2023.findings-acl)
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| Challenge: | Existing methods to construct multi-domain task-oriented dialogue systems are difficult to extend to new domains due to high cost of data annotation and scarcity of labeled dialogue data. |
| Approach: | They propose a domain attention module that uses distributional signatures to construct multi-domain dialogue systems with limited data. |
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Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models (2022.naacl-main)
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| Challenge: | Recent large-scale language models have produced human-like responses in open-domain dialogue systems. |
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Fully Statistical Neural Belief Tracking (P18-2)
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| Challenge: | Existing framework for a dialogue state tracking model requires an expensive manual retuning step . |
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Common Ground Tracking in Multimodal Dialogue (2024.lrec-main)
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Ibrahim Khalil Khebour, Kenneth Lai, Mariah Bradford, Yifan Zhu, Richard A. Brutti, Christopher Tam, Jingxuan Tu, Benjamin A. Ibarra, Nathaniel Blanchard, Nikhil Krishnaswamy, James Pustejovsky
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Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (C18-1)
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| Challenge: | Existing pipeline models for task-oriented dialogue system require explicit modeling of dialogue states and hand-crafted action spaces to query domain-specific knowledge base. |
| Approach: | They propose a framework that leverages the advantages of classic pipeline and sequence-to-sequence models. |
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