A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 13 May 2022.
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8 of 17 references.
References · 8
- 2016Neural Architectures for Named Entity Recognitioncited by 550
- 2017Beam Search Strategies for Neural Machine Translationcited by 232
- 2021ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfercited by 231
- 2019Text Classification Algorithms: A Surveycited by 163
- 2020Adding Chit-Chat to Enhance Task-Oriented Dialoguescited by 48
- 2021Few-shot Intent Classification and Slot Filling with Retrieved Examplescited by 35
- 2018AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experiencecited by 26
- 2021Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systemscited by 14
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Citing papers per year: 2023: 1.
1 paper.