Abstract: Systems and methods for an automated conversation with a transactional assistant are provided. This conversation relies upon initially a set of exchanges being defined. Each exchange connected to every other exchange by bidirectional edge transitions. A response from the conversation target is received, and is processed for natural language understanding (NLU) generate intents and entities. After the NLU, a determination is made which bidirectional edge transition applies, as a function of the intent and the source exchange. Subsequently, the exchange may be transitioned to a new exchange based upon the determined bidirectional edge transition, and a response is formulated using natural language generation (NLG) for the new exchange.

Abstract: Systems and methods for generating intents for a response is provided. The tokens of the response is encoded into a dense vector space as a plurality of vectors. Name entities are extracted, and individual sentences and paragraphs are both classified in response to the vectors. In addition to the tokens being represented in the vector space, the sentences and paragraphs may be represented in the vector space. The entities and intents are then used to determine an action for the system according to a policy that is optimized for. Annotations may be requested when the classifications are below thresholds, and these annotations may be employed in the action determination process. Annotation includes receiving an annotation work in an annotation queue, prioritizing the annotations, and sending the highest priority annotations to the annotator in order. This is used to update the production annotation database.