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. |
Similar Papers
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. |
Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent studies use in-context learning with large language models (LLM) to find similar dialogue exemplars for prompt learning. |
| Approach: | They propose to use a conversation retriever to find similar in-context examples for prompt learning. |
| Outcome: | The proposed approach improves on multiWOZ datasets with GPT-Neo-2.7B and LLaMA-7B/30B . |
Multi-Domain Dialogue State Tracking By Neural-Retrieval Augmentation (2022.findings-aacl)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework retrieves dialogue context from the index built using unstructured dialogue state and structured user/system utterances. |
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking (2024.eacl-long)
Copied to clipboard
| Challenge: | In-context learning with Large Language Models (LLMs) is a promising avenue of research in Dialog State Tracking (DST). |
| Approach: | They propose a data generation framework tailored for Dialog State Tracking that uses large language models to synthesize natural, coherent, and free-flowing dialogues with DST annotations. |
| Outcome: | The proposed framework improves joint goal accuracy by 4-5% over the zero-shot baseline on MultiWOZ 2.1 and 2.4. |
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. |
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. |
GCDST: A Graph-based and Copy-augmented Multi-domain Dialogue State Tracking (2020.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed model improves the performance of the multi-domain DST baseline with the absolute joint accuracy of 2.0% and 1.0% on the MultiWOZ 2.0 and 2.1 dialogue datasets. |
Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models (2025.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed model incorporates utterances of a second user into conversations, enabling a controlled evaluation of LLMs in multi-user settings. |
Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking (2023.findings-acl)
Copied to clipboard
| 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. |
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. |