BACKGROUND

Intelligent virtual assistants (IVAs) along with specialized agents for customer service and sales support are exploding in popularity. The continued adoption of IVAs is contributing to a growing problem directed to refining an IVA's knowledge effectively and efficiently. As IVA use as well as the number of tasks an IVA is expected to perform increases, there is a corresponding jump in the number of human-computer interactions to be reviewed for quality assurance. Therefore, discovering a means to expedite review and analysis of these interactions is critical.

Without scalable and efficient methods of automated conversation review, IVA designers must rely solely on human reviewers to validate expected behavior of the IVAs. As this is a manual and time consuming process, the reviewers are only able to view a limited number of interactions. The result is also subjective since reviewers may disagree on the user intention for any given turn in a conversation. In addition, as the IVA improves, errors in communication appear less often in a random sample due to their dwindling numbers. A recent challenge is public outcry over the human review of IVA conversation logs for the purpose of language understanding verification, due to privacy concerns.

SUMMARY

A scalable system provides automated conversation review that can identify potential miscommunications. The system may provide suggested actions to fix errors in IVA understanding, may prioritize areas of language model repair, and may automate the review of conversations. By the use of an automated system for conversation review, problematic interactions can be surfaced without exposing the entire set of conversation logs to human reviewers, thereby minimizing privacy invasion. A scalable system processes conversations and autonomously marks the interactions where the IVA is misunderstanding the user.

In an implementation, a method comprises receiving conversation data; determining that the conversation data has a misunderstood intent; determining an action to adjust a language model responsive to determining that the conversation data has the misunderstood intent; and providing the suggested action to an output device or to the language model.

In an implementation, a system comprises one or more processors; storage storing conversation data representing at least one conversation, and one or more indicators of intent error; memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the one or more processors to perform acts comprising: generating a risk score of a plurality of conversational turns in conversation data from conversations between a user and an intelligent virtual assistant; ranking the conversational turns by risk score for priority review; determining whether each of the conversational turns has a misunderstood intent; determining an action to adjust a language model responsive to determining whether each of the conversational turns has a misunderstood intent; and providing the suggested action to an output device or to the language model.

In an implementation, a method comprises processing conversation data with one or more natural language processing techniques to identify an intent for the conversation data, the intent being associated with a language model for the natural language processing techniques; determining, based at least in part on a presence of one or more indicators of intent error for the conversation data, a measure of confidence that the intent is correctly identified for the user input, and associating the measure of confidence with the conversation data; and determining an action to adjust the language model responsive the measure of confidence; and providing the action to an output device or to the language model.

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

