| Challenge: | Existing dialogue datasets contain lots of noise in their state annotations. |
| Approach: | They propose a framework to train robust dialogue state tracking models by combining pseudo and vanilla labels by a common weighting parameter. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy of 80.10% on multiWOZ 2.4. |
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ASSIST: Towards Label Noise-Robust Dialogue State Tracking (2022.findings-acl)
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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. |
| Outcome: | The proposed framework improves the goal accuracy of DST models by 28.16% on MultiWOZ 2.0 and 8.41% on MultiWoz 2.4, compared to using only the vanilla noisy labels. |
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems (2020.acl-main)
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| Challenge: | Existing methods to train a multi-domain dialogue state tracker are lacking in accuracy. |
| Approach: | They propose a Meta-Reinforced Multi-Domain State Generator to train a DST meta-learning model with a few domains as source domains and a new domain as target domain. |
| Outcome: | The proposed system outperforms the traditional training approach with extremely little training data in target domain. |
Robust Dialogue State Tracking with Weak Supervision and Sparse Data (2022.tacl-1)
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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. |
| Approach: | They propose a training strategy to build extractive DST models without the need for fine-grained manual span labels. |
| Outcome: | The proposed model improves robustness against sample sparsity, new concepts, and topics, leading to state-of-the-art performance on a range of benchmarks. |
Few Shot Dialogue State Tracking using Meta-learning (2021.eacl-main)
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| Challenge: | Existing methods for transferring knowledge from resource-rich domains to unknown domains are data hungry . a meta-learning algorithm is proposed to solve the problem of zero/few-shot DST . |
| Approach: | They propose a meta-learner for the problem of zero/few-shot DST . they propose to agnostically train any existing chatbot system to improve its performance . |
| Outcome: | The proposed meta-learner improves on baseline in a low-data setting. |
Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)
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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He
| Challenge: | Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state. |
| Approach: | They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states . |
| Outcome: | The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets. |
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. |
| Outcome: | The proposed model improves the performance of the multi-domain DST baseline with the absolute joint accuracy of 2.0% and 1.0% on the MultiWOZ 2.0 and 2.1 dialogue datasets. |
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. |
| Approach: | They propose a generalizable in-context tuning approach that retrieves highly relevant training examples for a given dialogue to fine-tune the model without any hand-crafted templates. |
| Outcome: | Experiments with the MultiWOZ benchmark datasets show that DiSTRICT outperforms existing approaches in various zero-shot and few-shot settings using a much smaller model. |
Out-of-Task Training for Dialog State Tracking Models (2020.coling-main)
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Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Carel van Niekerk, Milica Gasic
| Challenge: | Dialog state tracking (DST) suffers from data sparsity. |
| Approach: | They utilize non-dialog data from unrelated NLP tasks to train dialog state trackers . they propose to use dialog state tracking to summarise the conversation history . |
| Outcome: | The proposed method exploits non-dialog data from unrelated NLP tasks to train dialog state trackers. |
Enhancing Dialogue State Tracking Models through LLM-backed User-Agents Simulation (2024.acl-long)
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| Challenge: | Experimental results show that the model can be used to generate dialogues in new domains quickly. |
| Approach: | They propose to use LLMs to generate dialogue data to reduce dialogue collection and annotation costs. |
| Outcome: | The proposed model performs better than the baseline model trained on real data. |
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking (2023.findings-acl)
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| Challenge: | Experimental results show that MoNET outperforms previous DST methods in alleviating state momentum issues and improving the anti-noise ability. |
| Approach: | They propose to use previous state of each turn in training data as input to learn to predict current state. |
| Outcome: | The proposed model outperforms existing methods on multiWOZ datasets and shows that it can update and correct slot values and improve anti-noise ability. |