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. |
Similar Papers
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. |
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. |
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to track dialogue state are limited due to data sparsity and long dialogues. |
| Approach: | They propose to use the previous dialogue state and current dialogue utterance as input for DST. |
| Outcome: | The proposed approach outperforms existing methods and improves existing ones. |
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods to train a multi-domain dialogue state tracker are lacking in accuracy. |
| Approach: | They propose a Meta-Reinforced Multi-Domain State Generator to train a DST meta-learning model with a few domains as source domains and a new domain as target domain. |
| Outcome: | The proposed system outperforms the traditional training approach with extremely little training data in target domain. |
DiSTRICT: Dialogue State Tracking with Retriever Driven In-Context Tuning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to task-oriented conversation system DST use hand-crafted templates and additional slot information to fine-tune and prompt large pre-trained language models and elicit slot values from the dialogue context. |
| Approach: | They propose a generalizable in-context tuning approach that retrieves highly relevant training examples for a given dialogue to fine-tune the model without any hand-crafted templates. |
| Outcome: | Experiments with the MultiWOZ benchmark datasets show that DiSTRICT outperforms existing approaches in various zero-shot and few-shot settings using a much smaller model. |
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs (2024.findings-acl)
Copied to clipboard
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 . |
| Approach: | They propose a method that combines dialogue segmentation and state tracking within open-domain dialogues to improve long context tracking. |
| Outcome: | The proposed method outperforms the state-of-the-art on open-domain dialogue datasets and publicly available datasets. |
Multi-Domain Dialogue State Tracking with Disentangled Domain-Slot Attention (2023.findings-acl)
Copied to clipboard
| 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. |
Neural Dialogue State Tracking with Temporally Expressive Networks (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. |
| Approach: | They propose to combine temporal feature dependencies in spoken dialogues by using recurrent networks and probabilistic graphical models. |
| Outcome: | The proposed model improves turn-level-state prediction and state aggregation on standard datasets. |
Fully Statistical Neural Belief Tracking (P18-2)
Copied to clipboard
| Challenge: | Existing framework for a dialogue state tracking model requires an expensive manual retuning step . |
| Approach: | They propose to improve existing NBT model by removing a manual retuning step . they propose two different statistical update mechanisms to improve model performance . |
| Outcome: | The proposed model achieves competitive performance and provides a robust framework for building resource-light DST models. |
Continual Dialogue State Tracking via Example-Guided Question Answering (2023.emnlp-main)
Copied to clipboard
Hyundong Cho, Andrea Madotto, Zhaojiang Lin, Khyathi Chandu, Satwik Kottur, Jing Xu, Jonathan May, Chinnadhurai Sankar
| Challenge: | Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services causes catastrophic forgetting. |
| Approach: | They propose to reformulate dialogue state tracking (DST) as a bundle of example-guided question answering tasks to minimize the task shift between services. |
| Outcome: | The proposed model achieves state-of-the-art performance on DST continual learning metrics without relying on any complex regularization or parameter expansion methods. |