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.
- Task-Oriented Bots
- Training Data
ASJC Scopus subject areas
- Computer Networks and Communications