Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to enhance the zeroshot generalization of DST fail to effectively decouple semantics of samples, limiting the zero-shot performance of the system. |
| Approach: | They propose a new learning schema that explicitly disentangles the semantics of seen data and leverages the performance and robustness with the mixture-of-experts mechanism. |
| Outcome: | The proposed model achieves state-of-the-art on multiWOZ2.1 with 10M trainable parameters and is robust to the mixture-of experts mechanism. |
Similar Papers
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing zero-shot dialogue state tracking models suffer from domain transferring and partial prediction problems. |
| Approach: | They propose to establish connections between similar slots in different domains to improve model transfer performance in unseen domains. |
| Outcome: | Empirical results show that the proposed model achieves the goal accuracy of 57.13% on MultiWOZ2.1 and 55.4. |
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking (2024.findings-naacl)
Copied to clipboard
| 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. |
| Outcome: | The proposed method improves joint goal accuracy by 8% on general language models in zero-shot scenarios, and can be used in many domains. |
Zero-Shot Dialogue State Tracking via Cross-Task Transfer (2021.emnlp-main)
Copied to clipboard
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. |
| Approach: | They propose to transfer cross-task knowledge from general question answering corpora to QA model that can handle zero-shot DST. |
| Outcome: | The proposed model improves existing zero-shot and few-shot results on MultiWoz and shows better generalization ability in unseen domains. |
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking (2020.acl-main)
Copied to clipboard
| Challenge: | Existing techniques for zero-shot transfer learning for multi-domain dialogue state tracking are expensive and require human errors, delays in annotation, and normalization issues. |
| Approach: | They propose a zero-shot transfer learning technique where training data are synthesized from an abstract dialogue model and the ontology of the domain. |
| Outcome: | The proposed technique improves the state of the art on the multi-domain dialogue state tracking dataset by 21%. |
Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking (2024.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed approach can generate dialogues across 1,000+ domains with silver-standard dialogue state annotations and slot descriptions. |
From Schema to State: Zero-Shot Scheme-Only Dialogue State Tracking via Diverse Synthetic Dialogue and Step-by-Step Distillation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing research classifies zero-shot, scheme-only DST into two main types: the cross-domain scenario and the zero-schemaonly setting. |
| Approach: | They propose a zero-shot, scheme-only approach that generates synthetic dialogues that balance diversity with schema alignment and distills knowledge from a large language model into a smaller model. |
| Outcome: | The proposed approach achieves state-of-the-art performance under zero-shot, scheme-only situation and generalizes effectively to few-shot scenarios. |
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking (2022.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed method outperforms previous studies on few-shot dialogue state tracking in MultiWoZ 2.0 and 2.1 in cross-domain and multi-domain settings. |
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking (2022.aacl-short)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework achieves competitive performance in several few-shot scenarios. |
Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking (2022.coling-1)
Copied to clipboard
| Challenge: | Existing zero-shot learning methods ignore slot dependencies in a multidomain dialogue . experimental results show the effectiveness of our proposed method over existing state-of-art generation methods . |
| Approach: | They propose to use slot prompts combination, slot values demonstration and slot constraint object to model slot-slot dependency, slot-value dependency and slot-context dependency respectively. |
| Outcome: | The proposed method outperforms state-of-the-art methods under zero-shot/few-shot settings. |
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity? (2023.acl-short)
Copied to clipboard
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. |
| Approach: | They propose to use schema descriptions to facilitate zero-shot transfer to new domains . they argue that general purpose language models lack the ability to replace specialized systems . |
| Outcome: | The proposed method achieves state-of-the-art in zero-shot DST with in-context learning capabilities. |