Towards Interactive Guidance for Writing Training Utterances for Conversational Agents

Abstract

Improving conversational agents that are trained with supervised learning requires iteratively refining example intent training utterances based on chat log data. The difficulty of this process hinges on the quality of the initial example utterances used to train the intent before it was first deployed. Creating new intents from scratch, when conversation logs are not yet available, has many challenges. We interviewed experienced conversational agent intent trainers to better understand challenges they faced when creating new intents and their best practices for writing high quality training utterances. Using these findings and related literature, we developed an intent training tool that provided interactive guidance via either language feedback or sample utterances. Language feedback notified the user when training utterances could be linguistically improved, while sample utterances were crowdsourced and provided examples of end user language prior to deploying an intent. We compared these two types of guidance in a 187-participant between-subject study. We found that participants in the language feedback condition reported limited creativity and higher mental load and spent more time on the task, but were more thoughtful in crafting utterances that adhered to best practices. In contrast, sample utterance participants leveraged the samples to either quickly select examples or use them as a springboard to develop new utterance ideas. We report on differences in user experience in the strategies participants took and preferences for or against the different types of guidance.