User Utterance Acquisition for Training Task-Oriented Bots: A Review of Challenges, Techniques and Opportunities
By Mohammad-Ali Yaghoub-Zadeh-Fard, Boualem Benatallah, F. Casati, M. C. Barukh, Shayan Zamanirad. 2020
15 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Dialogue & Conversational AI
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In Artificial Intelligence
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- Zi Lin
- Jeremiah Zhe Liu
- David M. Blei
- Stefano Ermon
Citations in the corpus
- Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness (2020, methodology): “Detecting Out-of-Scope Intent in Conversational Language Understanding To validate the method beyond image modalities, we also evaluate SNGP on a practical language understanding task where uncertainty quantification is of natural importance: dialog intent detection [44, 78, 82, 84].”