@inproceedings{tian-etal-2022-q,
title = "{Q}-{TOD}: A Query-driven Task-oriented Dialogue System",
author = "Tian, Xin and
Lin, Yingzhan and
Song, Mengfei and
Bao, Siqi and
Wang, Fan and
He, Huang and
Sun, Shuqi and
Wu, Hua",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.489",
doi = "10.18653/v1/2022.emnlp-main.489",
pages = "7260--7271",
abstract = "Existing pipelined task-oriented dialogue systems usually have difficulties adapting to unseen domains, whereas end-to-end systems are plagued by large-scale knowledge bases in practice. In this paper, we introduce a novel query-driven task-oriented dialogue system, namely Q-TOD. The essential information from the dialogue context is extracted into a query, which is further employed to retrieve relevant knowledge records for response generation. Firstly, as the query is in the form of natural language and not confined to the schema of the knowledge base, the issue of domain adaption is alleviated remarkably in Q-TOD. Secondly, as the query enables the decoupling of knowledge retrieval from the generation, Q-TOD gets rid of the issue of knowledge base scalability. To evaluate the effectiveness of the proposed Q-TOD, we collect query annotations for three publicly available task-oriented dialogue datasets. Comprehensive experiments verify that Q-TOD outperforms strong baselines and establishes a new state-of-the-art performance on these datasets.",
}
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<abstract>Existing pipelined task-oriented dialogue systems usually have difficulties adapting to unseen domains, whereas end-to-end systems are plagued by large-scale knowledge bases in practice. In this paper, we introduce a novel query-driven task-oriented dialogue system, namely Q-TOD. The essential information from the dialogue context is extracted into a query, which is further employed to retrieve relevant knowledge records for response generation. Firstly, as the query is in the form of natural language and not confined to the schema of the knowledge base, the issue of domain adaption is alleviated remarkably in Q-TOD. Secondly, as the query enables the decoupling of knowledge retrieval from the generation, Q-TOD gets rid of the issue of knowledge base scalability. To evaluate the effectiveness of the proposed Q-TOD, we collect query annotations for three publicly available task-oriented dialogue datasets. Comprehensive experiments verify that Q-TOD outperforms strong baselines and establishes a new state-of-the-art performance on these datasets.</abstract>
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%0 Conference Proceedings
%T Q-TOD: A Query-driven Task-oriented Dialogue System
%A Tian, Xin
%A Lin, Yingzhan
%A Song, Mengfei
%A Bao, Siqi
%A Wang, Fan
%A He, Huang
%A Sun, Shuqi
%A Wu, Hua
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F tian-etal-2022-q
%X Existing pipelined task-oriented dialogue systems usually have difficulties adapting to unseen domains, whereas end-to-end systems are plagued by large-scale knowledge bases in practice. In this paper, we introduce a novel query-driven task-oriented dialogue system, namely Q-TOD. The essential information from the dialogue context is extracted into a query, which is further employed to retrieve relevant knowledge records for response generation. Firstly, as the query is in the form of natural language and not confined to the schema of the knowledge base, the issue of domain adaption is alleviated remarkably in Q-TOD. Secondly, as the query enables the decoupling of knowledge retrieval from the generation, Q-TOD gets rid of the issue of knowledge base scalability. To evaluate the effectiveness of the proposed Q-TOD, we collect query annotations for three publicly available task-oriented dialogue datasets. Comprehensive experiments verify that Q-TOD outperforms strong baselines and establishes a new state-of-the-art performance on these datasets.
%R 10.18653/v1/2022.emnlp-main.489
%U https://aclanthology.org/2022.emnlp-main.489
%U https://doi.org/10.18653/v1/2022.emnlp-main.489
%P 7260-7271
Markdown (Informal)
[Q-TOD: A Query-driven Task-oriented Dialogue System](https://aclanthology.org/2022.emnlp-main.489) (Tian et al., EMNLP 2022)
ACL
- Xin Tian, Yingzhan Lin, Mengfei Song, Siqi Bao, Fan Wang, Huang He, Shuqi Sun, and Hua Wu. 2022. Q-TOD: A Query-driven Task-oriented Dialogue System. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 7260–7271, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.