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. |
| Outcome: | The proposed method outperforms trained methods in the few-shot setting and requires little data and zero parameter updates. |
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| Challenge: | Existing methods for zero-shot and few-shot learning dialogue state tracking are hard and expensive. |
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| Challenge: | Prompt-based methods with large pre-trained language models have shown impressive unaided performance across many NLP tasks. |
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| Challenge: | In-context learning with Large Language Models (LLMs) is a promising avenue of research in Dialog State Tracking (DST). |
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ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity? (2023.acl-short)
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Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Milica Gasic
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| Challenge: | Existing methods for zero-shot dialogue state tracking (DST) ignore unlabelled data in the target domain. |
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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. |
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| Challenge: | Existing methods for zero-shot Dialogue State Tracking have focused on domaintransfers and have not yielded satisfactory results. |
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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. |
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