SynTOD: Augmented Response Synthesis for Robust End-to-End Task-Oriented Dialogue System (2024.lrec-main)
Copied to clipboard
| Challenge: | Task-oriented dialogue systems focus on training multiple tasks such as language understanding, tracking states, and generating appropriate responses to help users achieve their specific goals. |
| Approach: | They exploit the ability of pre-trained models to provide synthesis responses for fine-tuning end-to-end TOD systems. |
| Outcome: | The proposed model outperforms baseline models on multiwoz datasets and is available for further exploitation. |
Similar Papers
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. |
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. |
Synergizing In-context Learning with Hints for End-to-end Task-oriented Dialog Systems (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing end-to-end task-oriented dialogue systems require extensive training datasets to perform well. |
| Approach: | They propose a system that synergizes LLMs with task-specific hints to improve alignment in low-data settings. |
| Outcome: | The proposed model improves alignment in low-data settings while retaining competitive performance in full-data environments. |
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (2022.acl-long)
Copied to clipboard
| Challenge: | Existing pre-trained language models often form a cascaded generation problem . this can lead to error accumulation across different sub-tasks and greater data annotation overhead. |
| Approach: | They propose a plug-and-play model for task-oriented dialogue that learns primary TOD task completion skills from heterogeneous dialog corpora. |
| Outcome: | The proposed model learns primary TOD task completion skills from heterogeneous dialog corpora. |
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues (2023.emnlp-main)
Copied to clipboard
Sungryull Sohn, Yiwei Lyu, Anthony Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee
| Challenge: | Recent advances in task-oriented dialogue systems have limitations regarding transparency and controllability. |
| Approach: | They propose to infer the TOD-flow graph from dialog data annotated with dialog acts and integrate it with any dialogue model to improve its prediction performance, transparency, and controllability. |
| Outcome: | The proposed approach improves dialog act classification and response generation performance in the MultiWOZ and SGD benchmarks. |
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities (2024.emnlp-main)
Copied to clipboard
Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Current datasets cater to user-led systems and are limited to predefined specific scenarios and slots. |
| Approach: | They propose to use a Chinese dialogue dataset to train a model that authentically simulates human-computer dialogues in 30 popular life service scenarios. |
| Outcome: | The proposed model achieves a joint accuracy of 75.09% in out-of-domain evaluations . it also achieves notable abilities in slot filling and questioning . |
Rethinking Task-Oriented Dialogue Systems: From Complex Modularity to Zero-Shot Autonomous Agent (2024.acl-long)
Copied to clipboard
| Challenge: | Task-oriented dialogue systems are designed to be composed of several functional modules, but lacks a general-purpose instruction-following language model. |
| Approach: | They propose a fully zero-shot autonomous TOD agent that leverages a general-purpose instruction-following language model to decide what to do at each dialogue turn. |
| Outcome: | The proposed agent can perform tasks in real-life scenarios with a general-purpose instruction-following language model. |
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. |
Autoregressive Entity Generation for End-to-End Task-Oriented Dialog (2022.coling-1)
Copied to clipboard
| Challenge: | Task-oriented dialog systems require external knowledge base to generate a response . current systems require scanning the KB at each turn, which is inefficient when the kb scales up . |
| Approach: | They propose to generate entity autoregressively before leveraging it to guide response generation. |
| Outcome: | Experiments on MultiWOZ 2.1 single and CAMREST show that the proposed system generates more high-quality and entity-consistent responses in an end-to-end manner. |
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. |