| 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. |
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Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Milica Gasic
| Challenge: | Recent research on dialog state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. |
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| Outcome: | The proposed method achieves state-of-the-art in zero-shot DST with in-context learning capabilities. |
Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking (2023.findings-acl)
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| Challenge: | Recent work has demonstrated that in-context learning for dialogue state tracking outperforms training methods in the few-shot setting. |
| Approach: | They propose a method for in-context learning for dialogue state tracking that takes into account probabilities of competing surface forms and produces a more accurate dialogue state prediction. |
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MetaASSIST: Robust Dialogue State Tracking with Meta Learning (2022.emnlp-main)
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| 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. |
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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. |
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CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking (2022.aacl-short)
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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. |
| Approach: | They propose a few-shot dialogue state tracking framework that integrates self-training and self-supervised learning methods into the framework. |
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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. |
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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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Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking (2024.findings-emnlp)
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| Challenge: | Existing zero-shot dialogue state tracking datasets are limited in the number of domains and slot types they cover due to the high costs of data collection. |
| Approach: | They propose a fully automatic approach that generates synthetic zero-shot dialogue state tracking datasets. |
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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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S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs (2024.findings-acl)
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Sarkar Snigdha Sarathi Das, Chirag Shah, Mengting Wan, Jennifer Neville, Longqi Yang, Reid Andersen, Georg Buscher, Tara Safavi
| Challenge: | Dialogue state tracking (DST) was based on narrow task-oriented conversations . however, large language models have ushered in more flexible open-domain chat systems . |
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