Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems (P19-1)
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| Challenge: | Existing approaches to dialogue state tracking are dependent on domain ontology and lack of sharing knowledge across domains. |
| Approach: | They propose a transferable dialogue state generator that generates dialogue states from utterances using copy mechanism. |
| Outcome: | Empirical results show that TRADE achieves state-of-the-art 48.62% joint goal accuracy for the five domains of MultiWOZ. |
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| Challenge: | Existing approaches to dialogue state tracking are limited to scenarios with infinite slot values and prediction of unseen slot values. |
| Approach: | They propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. |
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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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| Challenge: | Existing techniques for zero-shot transfer learning for multi-domain dialogue state tracking are expensive and require human errors, delays in annotation, and normalization issues. |
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| Challenge: | Existing approaches to training DST on a single domain ignore information across domains. |
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| Challenge: | Existing models do not account for the dependencies between system and user utterances in the same turn and across different turns. |
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Zero-Shot Dialogue State Tracking via Cross-Task Transfer (2021.emnlp-main)
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Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung
| Challenge: | Existing approaches to training a dialogue state tracking model require extensive annotated dialogue data. |
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Dialogue State Tracking with Explicit Slot Connection Modeling (2020.acl-main)
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| Challenge: | Existing methods to track dialogue state are lacking in multi-domain scenarios. |
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| Challenge: | Multi-domain dialogue state tracking is a challenge for task-oriented dialogue systems . domains and slots are aggregated into a single query to generate domain-slot specific representations . |
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Generation and Extraction Combined Dialogue State Tracking with Hierarchical Ontology Integration (2021.emnlp-main)
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| Challenge: | Current models are not satisfactory for solving out-of-vocabulary problems . current models assume that the task ontology is well defined in advance . |
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Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking (2022.coling-1)
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| Challenge: | Existing zero-shot learning methods ignore slot dependencies in a multidomain dialogue . experimental results show the effectiveness of our proposed method over existing state-of-art generation methods . |
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