XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking (2023.findings-eacl)
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| Challenge: | Existing methods for capturing dialogue data are expensive and limited in their application. |
| Approach: | They propose a domain-agnostic extractive question answering approach with shared weights across domains to disentangle complex domain information in ToDs. |
| Outcome: | The proposed model can efficiently leverage domain-agnostic QA datasets while being domain-scalable and open vocabulary in DST. |
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Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung
| Challenge: | Existing approaches to training a dialogue state tracking model require extensive annotated dialogue data. |
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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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Zero-shot Generalization in Dialog State Tracking through Generative Question Answering (2021.eacl-main)
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| Challenge: | Existing methods for Dialog State Tracking do not generalize well to new domains and unseen slots. |
| Approach: | They propose an ontology-free framework that queries for unseen constraints and slots in multi-domain task-oriented dialogs using a conditional language model pre-trained on substantive English sentences. |
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A Zero-Shot Open-Vocabulary Pipeline for Dialogue Understanding (2025.naacl-long)
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| Challenge: | Existing approaches to DST are limited by their computational resources or lack flexibility to adapt to new slots. |
| Approach: | They propose a system that integrates domain classification and DST in a single pipeline and uses self-refining prompts to adapt dynamically. |
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Multi-Domain Dialogue State Tracking with Disentangled Domain-Slot Attention (2023.findings-acl)
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| Challenge: | Multi-domain dialogue state tracking is a challenge for task-oriented dialogue systems . domains and slots are aggregated into a single query to generate domain-slot specific representations . |
| Approach: | They propose to disentangle domain-slot attention for multi-domain dialogue state tracking by separating query about domains and slots from the attention component. |
| Outcome: | The proposed approach outperforms the standard multi-head attention with aggregated domain-slot query. |
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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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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Towards LLM-driven Dialogue State Tracking (2023.emnlp-main)
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| Challenge: | emergence of large language models (LLMs) such as GPT3 and ChatGPT has sparked considerable interest in assessing their efficacy across diverse applications. |
| Approach: | They present a framework for a domain-slot instruction tuning method that allows LDST to achieve performance on par with ChatGPT. |
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Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models (2025.findings-emnlp)
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| Challenge: | Large language models have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for task-specific training. |
| Approach: | They extend existing DST dataset by generating utterances of a second user based on speech act theory. |
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