Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems (N18-1)
Copied to clipboard
| Challenge: | Existing methods for learning task-oriented dialogues include applying reinforcement learning with user feedback on supervised pre-training models. |
| Approach: | They propose a hybrid imitation and reinforcement learning method that integrates user feedback and reinforcement training to improve the agent's performance. |
| Outcome: | The proposed method can learn from the mistake it makes via imitation learning from user teaching and feedback. |
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. |
Rethinking Supervised Learning and Reinforcement Learning in Task-Oriented Dialogue Systems (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Dialogue policy learning for task-oriented dialogue systems has enjoyed great progress through using reinforcement learning methods. |
| Approach: | They propose a dialogue action decoder and a simulator-free adversarial learning method to improve dialogue agent performance without using reinforcement learning. |
| Outcome: | The proposed methods achieve more stable and higher performance with fewer efforts, such as the domain knowledge required to design a user simulator and the intractable parameter tuning in reinforcement learning. |
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to training dialogue agents are supervised learning, but this is prohibitively expensive and time-consuming. |
| Approach: | They propose offline reinforcement learning methods that can be used to train dialogue agents . offline reinforcement learn methods can be combined with language models to yield realistic dialogue agents. |
| Outcome: | The proposed method can be combined with language models to produce realistic dialogue agents . the results show that the offline method can achieve the goal of the proposed system . |
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. |
Human-centric dialog training via offline reinforcement learning (2020.emnlp-main)
Copied to clipboard
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard
| Challenge: | a novel offline RL method can train dialog models to produce better conversations without the risk of humans teaching it harmful chat behaviors. |
| Approach: | They develop offline reinforcement learning algorithms that use human feedback to train dialog models . they use language similarity, laughter, sentiment, and more to identify positive feedback . |
| Outcome: | The proposed method improves on existing methods with 80 users in an open-domain setting. |
Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent work on end-to-end dialogue models with pre-trained dialogue corpora shows promising performance in the conversational system. |
| Approach: | They propose an end-to-end TOD system with task-optimized adapters which learn independently per task adding only small number of parameters after fixed layers of pre-trained network. |
| Outcome: | The proposed system achieves state-of-the-art performance on the MultiWOZ benchmark compared to existing models. |
Transferable Dialogue Systems and User Simulators (2021.acl-long)
Copied to clipboard
| Challenge: | a lack of training data is limiting the development of dialogue systems . we develop a framework for creating dialogue data through self-play between agents . |
| Approach: | They propose a framework that can incorporate new dialogue scenarios through self-play between two agents. |
| Outcome: | The proposed framework is highly effective in bootstrapping the performance of two agents in transfer learning. |
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning (2022.coling-1)
Copied to clipboard
| Challenge: | In task-oriented dialogue systems, the role of the natural language generation component is to convert a system's intentions, called dialogue acts (DAs), into natural language utterances and to convey DAs accurately to users. |
| Approach: | They propose a method for Adaptive Natural language generation for Task-Oriented dialogue via Reinforcement learning that incorporates a natural language understanding module into the objective function of RL. |
| Outcome: | The proposed method generates adaptive utterances against speech recognition errors and the different vocabulary levels of users in a multi-world task-oriented dialogue system. |
Learning Efficient Dialogue Policy from Demonstrations through Shaping (2020.acl-main)
Copied to clipboard
| Challenge: | Using reinforcement learning to learn dialogue policy requires a large volume of interactions with users. |
| Approach: | They propose a task-oriented dialogue agent that efficiently learns dialogue policy from demonstrations . they use an imitation model to distill knowledge from demonstration and reward shaping . |
| Outcome: | The proposed agent efficiently learns dialogue policy from demonstrations through policy shaping and reward shaping. |
Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems (2023.emnlp-main)
Copied to clipboard
| Challenge: | Current approaches to task-oriented dialogue systems integrate knowledge retrieval and response generation, which poses scalability challenges when dealing with extensive knowledge bases. |
| Approach: | They propose a retriever-generator architecture that harnesses a retrieval and a generator to generate system responses by using feedback from the generator as pseudo-labels. |
| Outcome: | The proposed architecture shows superior performance on three benchmark datasets. |