User Utterance Acquisition for Training Task-Oriented Bots: A Review of Challenges, Techniques and Opportunities

Mohammad Ali Yaghoub-Zadeh-Fard, Boualem Benatallah, Fabio Casati, Moshe Chai Barukh, Shayan Zamanirad

Research output: Contribution to journalReview article

Abstract

Building conversational task-oriented bots requires large and diverse sets of annotated user utterances to learn mappings between natural language utterances and user intents. Given the complexity of human language as well as the recent advances on intent recognition (especially deep-learning-based approaches), bot developers now have faced a new challenge: efficiently and effectively collecting a large number of quality (e.g., diverse and unbiased) training samples. This article studies training user utterance acquisition along several important dimensions including cost and quality. We discuss state of the art techniques, identify open issues, and inform an outlook on future research directions.

Original languageEnglish
Article number9022888
Pages (from-to)30-38
Number of pages9
JournalIEEE Internet Computing
Volume24
Issue number3
DOIs
Publication statusPublished - 1 May 2020
Externally publishedYes

Keywords

  • Task-Oriented Bots
  • Training Data

ASJC Scopus subject areas

  • Computer Networks and Communications

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