mlsanityData to 14 Oct 2026

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

arXiv 2205.06436 ↗First posted 13 May 2022cs.CLcited by 1

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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

  1. 2016Neural Architectures for Named Entity Recognitioncited by 550
  2. 2017Beam Search Strategies for Neural Machine Translationcited by 232
  3. 2021ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfercited by 231
  4. 2019Text Classification Algorithms: A Surveycited by 163
  5. 2020Adding Chit-Chat to Enhance Task-Oriented Dialoguescited by 48
  6. 2021Few-shot Intent Classification and Slot Filling with Retrieved Examplescited by 35
  7. 2018AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experiencecited by 26
  8. 2021Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systemscited by 14

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