@inproceedings{zhu-etal-2023-convlab,
title = "{C}onv{L}ab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format",
author = "Zhu, Qi and
Geishauser, Christian and
Lin, Hsien-chin and
van Niekerk, Carel and
Peng, Baolin and
Zhang, Zheng and
Feng, Shutong and
Heck, Michael and
Lubis, Nurul and
Wan, Dazhen and
Zhu, Xiaochen and
Gao, Jianfeng and
Gasic, Milica and
Huang, Minlie",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.9",
doi = "10.18653/v1/2023.emnlp-demo.9",
pages = "106--123",
abstract = "Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants. Existing toolkits for building TOD systems often fall short in delivering comprehensive arrays of data, model, and experimental environments with a user-friendly experience. We introduce ConvLab-3: a multifaceted dialogue system toolkit crafted to bridge this gap. Our unified data format simplifies the integration of diverse datasets and models, significantly reducing complexity and cost for studying generalization and transfer. Enhanced with robust reinforcement learning (RL) tools, featuring a streamlined training process, in-depth evaluation tools, and a selection of user simulators, ConvLab-3 supports the rapid development and evaluation of robust dialogue policies. Through an extensive study, we demonstrate the efficacy of transfer learning and RL and showcase that ConvLab-3 is not only a powerful tool for seasoned researchers but also an accessible platform for newcomers.",
}
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<abstract>Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants. Existing toolkits for building TOD systems often fall short in delivering comprehensive arrays of data, model, and experimental environments with a user-friendly experience. We introduce ConvLab-3: a multifaceted dialogue system toolkit crafted to bridge this gap. Our unified data format simplifies the integration of diverse datasets and models, significantly reducing complexity and cost for studying generalization and transfer. Enhanced with robust reinforcement learning (RL) tools, featuring a streamlined training process, in-depth evaluation tools, and a selection of user simulators, ConvLab-3 supports the rapid development and evaluation of robust dialogue policies. Through an extensive study, we demonstrate the efficacy of transfer learning and RL and showcase that ConvLab-3 is not only a powerful tool for seasoned researchers but also an accessible platform for newcomers.</abstract>
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%0 Conference Proceedings
%T ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
%A Zhu, Qi
%A Geishauser, Christian
%A Lin, Hsien-chin
%A van Niekerk, Carel
%A Peng, Baolin
%A Zhang, Zheng
%A Feng, Shutong
%A Heck, Michael
%A Lubis, Nurul
%A Wan, Dazhen
%A Zhu, Xiaochen
%A Gao, Jianfeng
%A Gasic, Milica
%A Huang, Minlie
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhu-etal-2023-convlab
%X Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants. Existing toolkits for building TOD systems often fall short in delivering comprehensive arrays of data, model, and experimental environments with a user-friendly experience. We introduce ConvLab-3: a multifaceted dialogue system toolkit crafted to bridge this gap. Our unified data format simplifies the integration of diverse datasets and models, significantly reducing complexity and cost for studying generalization and transfer. Enhanced with robust reinforcement learning (RL) tools, featuring a streamlined training process, in-depth evaluation tools, and a selection of user simulators, ConvLab-3 supports the rapid development and evaluation of robust dialogue policies. Through an extensive study, we demonstrate the efficacy of transfer learning and RL and showcase that ConvLab-3 is not only a powerful tool for seasoned researchers but also an accessible platform for newcomers.
%R 10.18653/v1/2023.emnlp-demo.9
%U https://aclanthology.org/2023.emnlp-demo.9
%U https://doi.org/10.18653/v1/2023.emnlp-demo.9
%P 106-123
Markdown (Informal)
[ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format](https://aclanthology.org/2023.emnlp-demo.9) (Zhu et al., EMNLP 2023)
ACL
- Qi Zhu, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Baolin Peng, Zheng Zhang, Shutong Feng, Michael Heck, Nurul Lubis, Dazhen Wan, Xiaochen Zhu, Jianfeng Gao, Milica Gasic, and Minlie Huang. 2023. ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 106–123, Singapore. Association for Computational Linguistics.