Papers with JGA
Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking (2023.acl-long)
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| Challenge: | Existing schema-guided dialogue state tracking models do not account for schema variations and are not generalized to unseen services. |
| Approach: | They propose a new architecture which allows for rich attention among descriptions and history while keeping computation costs constrained. |
| Outcome: | The proposed model outperforms the more than 30x larger D3ST-XXL model on the SGD-X benchmark by 5.0 points. |
Towards Fair Evaluation of Dialogue State Tracking by Flexible Incorporation of Turn-level Performances (2022.acl-short)
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| Challenge: | Dialogue State Tracking (DST) is a task-oriented conversational agent that keeps track of key information exchanged during a conversation. |
| Approach: | They propose a new evaluation metric called Flexible Goal Accuracy to address shortcomings of JGA. |
| Outcome: | The proposed metric improves on existing metrics and improves performance of turn-level and non-cumulative belief state models. |
TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation (2024.acl-long)
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| Challenge: | Current methods for Continual Dialogue State Tracking (DST) struggle with catastrophic forgetting and knowledge transfer between tasks. |
| Approach: | They propose a framework for task skill localization and consolidation that enables effective knowledge transfer without relying on memory replay. |
| Outcome: | The proposed framework shows a 7.6% increase in Avg. JGA and 11% rise in BWT metrics over existing state-of-the-art methods. |
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. |
More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking (2023.findings-eacl)
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| Challenge: | Pre-trained language models (PLMs) can track only slots drawn from a database or domain ontology. |
| Approach: | They propose a framework for generating synthetic schemas which uses tree-based ranking to optimise lexical diversity and semantic faithfulness. |
| Outcome: | The proposed framework improves the generalisation of strong baselines by augmenting training data with prompts generated by the framework. |
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. |
| Outcome: | The proposed system improves on existing methods on multiWOZ datasets and provides 20% better Joint Goal Accuracy (JGA) over existing methods with 90% fewer requests to the LLM API. |
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling (2024.acl-long)
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Zekun Li, Zhiyu Chen, Mike Ross, Patrick Huber, Seungwhan Moon, Zhaojiang Lin, Xin Dong, Adithya Sagar, Xifeng Yan, Paul Crook
| Challenge: | Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. |
| Approach: | They propose a method for solving dialogue state tracking (DST) with large language models through function calling. |
| Outcome: | The proposed approach improves zero-shot DST, allowing adaptation to diverse domains without extensive data collection or model tuning. |
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking (2023.findings-emnlp)
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| Challenge: | Existing methods for zero-shot Dialogue State Tracking have focused on domaintransfers and have not yielded satisfactory results. |
| Approach: | They propose a new In-Context Learning method to introduce additional updating strategies in zero-shot DST by leveraging powerful Large Language Models and translating the original dialogue to JSON through semantic parsing as an intermediate state. |
| Outcome: | The proposed method outperforms existing zero-shot DST methods on MultiWOZ, showing significant improvements in JGA and slot accuracy compared to existing methods. |
Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented Conversational AI Through Accountability Modeling (2025.acl-long)
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| Challenge: | Recent LLMs are known to hallucinate, producing responses that seem plausible but are factually incorrect. |
| Approach: | They propose an accountability model for LLM-based task-oriented dialogue agents to address user overreliance via friction turns in cases of model uncertainty and errors associated with dialogue state tracking (DST). |
| Outcome: | The proposed model improves joint goal accuracy (JGA) of DST output by 3% on two established benchmarks. |