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A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example

Published: 07 July 2022 Publication History

Abstract

Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e.g., intents and slots); (ii) poor controllability and interpretability; (iii) annotation-hungry. In this paper, we propose to represent utterance with a simpler concept named Dialogue Action, upon which we construct a tree-structured TaskFlow and further build task-oriented chatbot with TaskFlow as core component. A framework is presented to automatically construct TaskFlow from large-scale dialogues and deploy online. Our experiments on real-world after-sale customer services show TaskFlow can satisfy the major needs, as well as reduce the developer burden effectively.

References

[1]
Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel- Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien- Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, and Eddie Wang. 2021. Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations. Association for Computational Linguistics, Online, 125--132. https://doi.org/10.18653/v1/2021.naacl-demos.15
[2]
Markus Freitag and Yaser Al-Onaizan. 2017. Beam Search Strategies for Neural Machine Translation. In Proceedings of the First Workshop on Neural Machine Translation. 56--60.
[3]
Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura Barnes, and Donald Brown. 2019. Text classification algorithms: A survey. Information 10, 4 (2019), 150.
[4]
K Krishna and M Narasimha Murty. 1999. Genetic K-means algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 29, 3 (1999), 433--439.
[5]
Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016).
[6]
Feng-Lin Li, Minghui Qiu, Haiqing Chen, Xiongwei Wang, Xing Gao, Jun Huang, Juwei Ren, Zhongzhou Zhao, Weipeng Zhao, Lei Wang, et al. 2017. Alime assist: An intelligent assistant for creating an innovative e-commerce experience. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2495--2498.
[7]
Chenxu Lv, Hengtong Lu, Shuyu Lei, Huixing Jiang, Wei Wu, Caixia Yuan, and Xiaojie Wang. 2021. Task-Oriented Clustering for Dialogues. In Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, Punta Cana, Dominican Republic, 4338--4347. https://doi.org/10.18653/v1/2021.findings-emnlp.368
[8]
Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.
[9]
John R Searle. 1975. A taxonomy of illocutionary acts. (1975).
[10]
Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller, Becka Silvert, Bing Liu, Zhiguang Wang, Honglei Liu, Eunjoon Cho, and Claire Cardie. 2021. Adding Chit-Chat to Enhance Task-Oriented Dialogues. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 1570--1583. https://doi.org/10.18653/v1/2021.naacl-main.124
[11]
David R Traum and Elizabeth A Hinkelman. 1992. Conversation acts in taskoriented spoken dialogue. Computational intelligence 8, 3 (1992), 575--599.
[12]
Xiangyu Xi, Wei Ye, Tong Zhang, Quanxiu Wang, Shikun Zhang, Huixing Jiang, and Wei Wu. 2021. Improving event detection by exploiting label hierarchy. In ICASSP 2021--2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 7688--7692.
[13]
Xi Xiangyu, Zhang Tong, Ye Wei, Zhang Jinglei, Xie Rui, and Zhang Shikun.2019. A hybrid character representation for chinese event detection. In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 1--8.
[14]
Yuanmeng Yan, Rumei Li, Sirui Wang, Fuzheng Zhang, Wei Wu, and Weiran Xu. 2021. ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Online, 5065--5075. https://doi.org/10.18653/v1/2021.acl-long.393
[15]
Kaisheng Yao, Geoffrey Zweig, Mei-Yuh Hwang, Yangyang Shi, and Dong Yu. 2013. Recurrent neural networks for language understanding. In Interspeech. 2524--2528.
[16]
Dian Yu, Luheng He, Yuan Zhang, Xinya Du, Panupong Pasupat, and Qi Li. 2021. Few-shot Intent Classification and Slot Filling with Retrieved Examples. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 734--749. https://doi.org/10.18653/v1/2021.naacl-main.59
[17]
Xiaoming Zhu. 2019. Case ii (part a): Jimi's growth path: Artificial intelligence has redefined the customer service of jd. com. In Emerging Champions in the Digital Economy. Springer, 91--103.

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  • (2023)Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level GenerationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591832(3255-3259)Online publication date: 19-Jul-2023

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cover image ACM Conferences
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2022

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Author Tags

  1. dialogue system
  2. retrieval-based method
  3. taskflow

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  • (2023)Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level GenerationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591832(3255-3259)Online publication date: 19-Jul-2023

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