Abstract: A system includes a processor that executes computer executable components stored in a memory. The computer executable components can comprise a prompt generation component that builds, based on usage logs, a ranking of slots, for filling in conversations, and dynamically and incrementally prompts a subset of slots based on the ranking for a virtual assistant. The computer executable components comprise a hybrid grounding component that grounds information pertaining to the subset of slots for the virtual assistant, wherein the hybrid grounding component utilizes intent determination to fill one or more of the slots.

Abstract: A method, a structure, and a computer system for OOD sentence detection in dialogue systems. The exemplary embodiments may include receiving, for a domain corresponding to a particular topic, one or more on-topic text inputs and one or more off-topic text inputs. The exemplary embodiments may further include encoding the one or more on-topic text inputs and the one or more off-topic text inputs into a latent space, as well as decoding the one or more on-topic text inputs and the one or more off-topic text inputs from the latent space. The exemplary embodiments may additionally include minimizing a reconstruction error between the encoded one or more on-topic text inputs and the decoded one or more on-topic text inputs, and maximizing a reconstruction error between the encoded one or more off-topic text inputs and the decoded one or more off-topic text inputs.

Abstract: A first intent from customer provided data is encoded as an intent embedding and the intent embedding of the first intent and an intent embedding corresponding to one or more items of a training workspace are compared to generate a similarity score. The first intent is mapped to a similar item of the one or more items and a corresponding count of the similar item is incremented by one in response to the similarity score being greater than a given threshold. A matrix is created based on the similarity score. At least a first machine learning model is trained using one or more of the training workspaces of the created matrix; at least a second machine learning model is trained using the created matrix; and deployment of the at least second machine learning model is facilitated for performing inferencing.

Abstract: Methods, systems, and computer program products for configuring artificial intelligence-based virtual assistants using response modes are provided herein.

Abstract: A method, a structure, and a computer system for OOD sentence detection in dialogue systems. The exemplary embodiments may include receiving, for a domain corresponding to a particular topic, one or more on-topic text inputs and one or more off-topic text inputs. The exemplary embodiments may further include encoding the one or more on-topic text inputs and the one or more off-topic text inputs into a latent space, as well as decoding the one or more on-topic text inputs and the one or more off-topic text inputs from the latent space. The exemplary embodiments may additionally include minimizing a reconstruction error between the encoded one or more on-topic text inputs and the decoded one or more on-topic text inputs, and maximizing a reconstruction error between the encoded one or more off-topic text inputs and the decoded one or more off-topic text inputs.

Abstract: A method, system, and computer-usable medium are disclosed for identifying areas to improve an interactive conversational system, such as a chatbot. A stream of stream of conversational interactions C (C1, C2, . . . , Cn) between users and the interactive conversational system is received. An intent clustering model is periodically applied to the stream to form an incremental clustering based on a set of derived intents to form a mapping from a first set of conversational characteristics to a first set of intents and a second set of conversational characteristics to a first set of unclear intents. Information is provided related to the second set of conversation characteristics.

Abstract: A method, system, and computer-usable medium are disclosed for identifying areas to improve an interactive conversational system, such as a chatbot. A stream of stream of conversational interactions C (C1, C2, . . . , Cn) between users and the interactive conversational system is received. An intent clustering model is periodically applied to the stream to form an incremental clustering based on a set of derived intents to form a mapping from a first set of conversational characteristics to a first set of intents and a second set of conversational characteristics to a first set of unclear intents. Information is provided related to the second set of conversation characteristics.