| Challenge: | Existing methods for dialog learning assume there is only one correct next utterance . a significant drop in performance is seen in existing methods for evaluating dialog systems . |
| Approach: | They propose a method that assumes there is only one correct next utterance in a dialog . they propose bAbI dialog tasks that introduce valid next . |
| Outcome: | The proposed method improves performance and achieves 47.3% accuracy on permuted-bAbI dialog tasks. |
Similar Papers
End-to-End Learning of Task-Oriented Dialogs (N18-4)
Copied to clipboard
| Challenge: | Dissertation addresses the limitations of conventional task-oriented dialog systems . conventions of such systems include a complex pipeline and dialog state tracking . |
| Approach: | They propose a neural network based dialog system that can robustly track dialog state . they propose offline training and online interactive learning methods to improve efficiency . |
| Outcome: | The proposed system can track dialog state, interface with knowledge bases, and integrate structured query results into system responses to successfully complete task-oriented dialog. |
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment (2020.acl-main)
Copied to clipboard
| Challenge: | Existing end-to-end dialog systems perform less effectively when data is scarce. |
| Approach: | They propose a Meta-Dialog System which combines meta-learning and human-machine collaboration to improve dialog learning by a new extended-bAbI dataset and a transformed MultiWOZ dataset. |
| Outcome: | The proposed system outperforms non-meta-learning baselines on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. |
Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Using large pre-trained language models for end-to-end TOD modeling has made significant progress on benchmarks . a paradigm of leveraging large pretrained models has shown promising results . |
| Approach: | They combine paradigm of leveraging large pre-trained language models with multi-task learning framework . their model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 . |
| Outcome: | The proposed model achieves state-of-the-art results on multiWOZ 2.0 and MultiWOZ 2.1 . it also improves generalization capability through domain adaptation experiments in the few-shot setting. |
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)
Copied to clipboard
| Challenge: | End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity. |
| Approach: | They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends. |
| Outcome: | The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. |
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 (2020.acl-main)
Copied to clipboard
| Challenge: | End-to-end dialogue systems with monolithic neural architecture are often trained with input-output utterances without taking into account the entire annotations available in the corpus. |
| Approach: | They propose an end-to-end neural architecture for goal-oriented dialogue systems that addresses both challenges . they propose a modular architecture where modules are optimized individually . |
| Outcome: | The proposed system achieved the top position in the human evaluation task . it is based on a neural architecture that can be integrated with external systems . |
Comet: Dialog Context Fusion Mechanism for End-to-End Task-Oriented Dialog with Multi-task Learning (2025.coling-main)
Copied to clipboard
| Challenge: | Existing end-to-end task-oriented dialog systems often encounter challenges arising from implicit information, coreference, and the presence of noisy and irrelevant data within the dialog context. |
| Approach: | They propose a dialog context fusion mechanism for end-to-end task-oriented dialog augmented with three additional tasks: dialog summarization, domain prediction, and slot detection. |
| Outcome: | The proposed method achieves state-of-the-art on the MultiWOZ and CrossWOZ datasets. |
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog (D19-1)
Copied to clipboard
| Challenge: | Existing methods to learn dialog policy require elaborate design and user goals. |
| Approach: | They propose an algorithm that estimates the reward signal and infers the user goal in dialog sessions. |
| Outcome: | The proposed algorithm achieves higher task success than state-of-the-art models on a multi-domain task-oriented dialog dataset. |
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog (2023.findings-acl)
Copied to clipboard
| Challenge: | Empirical results show dialog context representations are more conducive to multi-turn task-oriented dialog. |
| Approach: | They propose an end-to-end task-oriented dialog system with two contrastive learning strategies to model relationship between dialog context and belief/action state representations. |
| Outcome: | Empirical results show that dialog context representations are more conducive to multi-turn task-oriented dialog. |
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals (2022.acl-long)
Copied to clipboard
| Challenge: | a dialog system posits that users have figured out clear and specific goals . but in many real-world scenarios, users struggle to figure out specific goals by determining all the necessary slots. |
| Approach: | They propose a mixed-type dialog model with a Prompt-based continual learning mechanism . they collect 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains . |
| Outcome: | The proposed model provides user-goal-related knowledge to help figure out clear and specific goals . it can be extended to any specific type by utilizing existing dialog corpora effectively. |
Goal-oriented Vision-and-Dialog Navigation via Reinforcement Learning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for vision-and-dialog navigation are limited and do not adapt to domain changes. |
| Approach: | They propose a problem where an agent computes dialog-navigation policies from trial and error. |
| Outcome: | The proposed agent outperforms baselines in success rate in photo-realistic simulations. |