Improving Limited Labeled Dialogue State Tracking with Self-Supervision (2020.findings-emnlp)
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| Challenge: | Existing dialogue state tracking models require plenty of labeled data, but collecting labels is expensive. |
| Approach: | They propose to use only 1% labeled data to train dialogue state tracking models . they encourage a model to have consistent latent distributions given a perturbed input . |
| Outcome: | The proposed self-supervised signals improve goal accuracy by 8.95% when only 1% labeled data is used on the MultiWOZ dataset. |
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| Challenge: | Existing versions of MultiWOZ 2.0 have been published, but there are still lots of noisy labels in the training set. |
| Approach: | They propose a framework to train dialogue state tracking models from noisy labels instead of improving annotation quality further by using auxiliary models. |
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Michael Heck, Nurul Lubis, Carel van Niekerk, Shutong Feng, Christian Geishauser, Hsien-Chin Lin, Milica Gašić
| Challenge: | Generalizing dialogue state tracking (DST) to new data and domains is especially challenging due to the strong reliance on abundant and fine-grained supervision during training. |
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| Challenge: | Existing few-shot dialogue state tracking (DST) methods transfer knowledge from labeled data into DST, but collecting large amount of labeles is laborious. |
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GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking (2020.findings-emnlp)
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| Challenge: | Existing approaches to training DST on a single domain ignore information across domains. |
| Approach: | They construct a dialogue state graph to transfer structured features among related domain-slot pairs across domains and encode the graph information of dialogue states by graph convolutional networks. |
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Multi-Domain Dialogue State Tracking By Neural-Retrieval Augmentation (2022.findings-aacl)
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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. |
| Approach: | They propose to create a Neural Index based on dialogue context by analyzing user dialogue and previous turn state and generating a retrieval-guided generation approach. |
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UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking (2024.findings-naacl)
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| Challenge: | Existing methods for zero-shot dialogue state tracking (DST) ignore unlabelled data in the target domain. |
| Approach: | They propose to transform zero-shot dialogue state tracking into few-shot DST by utilising unlabelled data via joint and self-training methods. |
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Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking (2022.findings-acl)
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| Challenge: | Annotating task-oriented dialogues is notorious for the expensive and difficult data collection process. |
| Approach: | They propose to reformulate dialogue state tracking as a dialogue summarization problem by using synthetic dialogue summaries generated by a set of rules. |
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| Challenge: | Recent advances in Large Language Models (LLMs) have notably enhanced task-oriented dialogue systems, particularly in Dialogue State Tracking (DST). |
| Approach: | They propose a group-relative policy optimization method that guides LLMs toward improved DST accuracy even under low-resource conditions. |
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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. |
| Approach: | They propose to use the previous dialogue state and current dialogue utterance as input for DST. |
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DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning (2023.emnlp-main)
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| Challenge: | Existing approaches to task-oriented conversation system DST use hand-crafted templates and additional slot information to fine-tune and prompt large pre-trained language models and elicit slot values from the dialogue context. |
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