Continual Dialogue State Tracking via Reason-of-Select Distillation (2024.findings-acl)
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| Challenge: | Existing research on dialogue systems has focused on domain-specific offline systems lacking adaptation abilities. |
| Approach: | They propose a Reason-of-Select distillation method that enhances smaller models with a novel "meta-reasoning" capability. |
| Outcome: | Experiments show that the proposed method significantly improves the performance and generalization capabilities of existing models. |
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| Challenge: | Existing approaches to training DST on a single domain ignore information across domains. |
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| Challenge: | Existing approaches for DST are conditioned on previous dialogue states, but the dependency on previous dialogs makes it difficult to prevent error propagation to subsequent turns. |
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Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking (2020.findings-emnlp)
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| Challenge: | Existing methods to track dialogue state are limited due to data sparsity and long dialogues. |
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