Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking (2021.acl-short)
Copied to clipboard
| Challenge: | Existing dialog state tracking models neglect rich structural information in a dataset. |
| Approach: | They propose to use curriculum learning to leverage dialog state tracking data . they propose a model-agnostic framework that pre-trains a DST model with schema information . |
| Outcome: | The proposed framework improves performance over a transformer-based and RNN-based model on WOZ2.0 and MultiWOZ2.1. |
Similar Papers
Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System (2021.findings-acl)
Copied to clipboard
| Challenge: | et al., 2013) show that dialog policy learning is an important component of the task-oriented dialogue system. |
| Approach: | They propose a framework that integrates curriculum learning and policy optimization . they propose to train dialog agents from easy dialogues to complex ones . |
| Outcome: | The proposed framework outperforms the state-of-the-art model on multi-task dialogues. |
Out-of-Task Training for Dialog State Tracking Models (2020.coling-main)
Copied to clipboard
Michael Heck, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Marco Moresi, Carel van Niekerk, Milica Gasic
| Challenge: | Dialog state tracking (DST) suffers from data sparsity. |
| Approach: | They utilize non-dialog data from unrelated NLP tasks to train dialog state trackers . they propose to use dialog state tracking to summarise the conversation history . |
| Outcome: | The proposed method exploits non-dialog data from unrelated NLP tasks to train dialog state trackers. |
Span-Selective Linear Attention Transformers for Effective and Robust Schema-Guided Dialogue State Tracking (2023.acl-long)
Copied to clipboard
| 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. |
Knowledge-Aware Graph-Enhanced GPT-2 for Dialogue State Tracking (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for dialogue state tracking are based on Graph Attention Networks . if the relationship between slots and values is modelled explicitly, this can be improved . |
| Approach: | They propose a model architecture that augments GPT-2 with Graph Attention Networks to allow sequential prediction of slot values. |
| Outcome: | The proposed architecture improves performance against a strong GPT-2 baseline and with sparsely supervised training. |
Robust Dialogue State Tracking with Weak Supervision and Sparse Data (2022.tacl-1)
Copied to clipboard
Michael Heck, Nurul Lubis, Carel van Niekerk, Shutong Feng, Christian Geishauser, Hsien-Chin Lin, Milica Gašić
| Challenge: | Generalizing dialogue state tracking (DST) to new data and domains is especially challenging due to the strong reliance on abundant and fine-grained supervision during training. |
| Approach: | They propose a training strategy to build extractive DST models without the need for fine-grained manual span labels. |
| Outcome: | The proposed model improves robustness against sample sparsity, new concepts, and topics, leading to state-of-the-art performance on a range of benchmarks. |
Schema Encoding for Transferable Dialogue State Tracking (2022.coling-1)
Copied to clipboard
| Challenge: | Recent work has focused on deep neural models for task-oriented dialogue systems . however, the neural models require a large dataset for training and a new dataset to be trained on another domain. |
| Approach: | They propose a schema encoder for transferable dialogue state tracking to new domains . they aim to transfer the model to new datasets by encoding new schemas based on the dataset . |
| Outcome: | The proposed method improves the accuracy of the proposed model on multi-domain settings. |
MetaASSIST: Robust Dialogue State Tracking with Meta Learning (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing dialogue datasets contain lots of noise in their state annotations. |
| Approach: | They propose a framework to train robust dialogue state tracking models by combining pseudo and vanilla labels by a common weighting parameter. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy of 80.10% on multiWOZ 2.4. |
Knowledge-grounded Dialog State Tracking (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Structured knowledge is encoded implicitly into model parameters for downstream tasks, making training inefficient. |
| Approach: | They propose to perform dialog state tracking grounded on knowledge encoded externally. |
| Outcome: | The proposed method outperforms baseline models in the few-shot learning setting. |
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. |
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking (2023.findings-eacl)
Copied to clipboard
| 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. |