Abstract: An example system includes a processor to receive a summary of a conversation to be generated. The processor can input the summary into a trained summary-grounded conversation generator. The processor can receive a generated conversation from the trained summary-grounded conversation generator.

Abstract: Audio signals representing a current utterance in a conversation and a dialog history including at least information associated with past utterances corresponding to the current utterance in the conversation can be received. The dialog history can be encoded into an embedding. A spoken language understanding neural network model can be trained to perform a spoken language understanding task based on input features including at least speech features associated with the received audio signals and the embedding. An encoder can also be trained to encode a given dialog history into an embedding. The spoken language understanding task can include predicting a dialog action of an utterance. The spoken language understanding task can include predicting a dialog intent or overall topic of the conversation.

Abstract: A processor may receive first voice data associated with a first user utterance in conversation in a guided dialog system. The processor may identify from the first voice data a first topic of a set of topics associated with the first user utterance. The processor may identify a first solution associated with the first topic, the first solution having one or more solution segments for accomplishing a task related to the topic. The processor may generate a first response for a second user based on a first solution segment of the first solution and the first voice data.

Abstract: Methods, systems, and computer program products for generating dialog system workspaces are provided herein. A computer-implemented method includes obtaining (i) a set of policy documents and (ii) a set of initial questions; identifying at least one of the policy documents in the set of policy documents that is relevant to answering a given one of the initial questions in the set of initial questions; generating, based at least in part on an analysis of said identified policy document, (i) at least one follow-up question to said given initial question and (ii) two or more candidate answers to said at least one follow-up question; generating a dialog tree comprising at least (i) a parent node corresponding to the at least one follow-up question and (ii) child nodes corresponding to the two or more candidate answers; translating the dialog tree into a dialog workspace; and deploying the dialog workspace in an intelligent dialog system.

Abstract: Embodiments of the invention are directed to a computer-implemented method of responding to an inquiry received electronically at a synchronous conferencing system (SCS). A non-limiting example of the computer-implemented method includes, based at least in part on the inquiry, using a processor of the SCS to generate a script having one or more script computer instructions. The processor is used to execute the script computer instructions to generate script computer instruction results, wherein the script computer instruction results include inquiry response information that is responsive to the inquiry. Based at least in part on the inquiry and the inquiry response information, the processor is used to generate an inquiry response that is responsive to the inquiry.

Abstract: In an approach to creation and execution of dialog shortcuts, responsive to detecting initiation of a dialog, an utterance is received from a user. Whether the utterance contains an objective of the user is determined, where the objective is chosen from a group including create a shortcut, execute the shortcut, modify the shortcut, and delete the shortcut. Responsive to determining that the utterance contains the objective, the objective is implemented.

Abstract: Techniques facilitating interpretation of deep neural model based dialogue agents are provided. In one example, a computer-implemented method comprises extracting, by a device operatively coupled to a processor, features from a dialogue model independently from the dialogue model, the features comprising input features provided to the dialogue model and output features produced via the dialogue model in response to the input features, resulting in extracted features; and analyzing, by the device, a dialogue context associated with the extracted features by identifying pairwise interactions between respective ones of the extracted features.

Abstract: Embodiments herein describe an automated process for identifying tasks that can be converted from primarily being driven by a human agent into bot-driven RPAs. This disclosure describes techniques for automatically generating RPAs by monitoring the actions of the agent and identifying corresponding events. These events can be performed on unstructured data sources and structured data sources. The events are clustered into different tasks where pattern detection techniques are used to identify common (or overlapping) events and causal relationships between the events. Using these causal relationships, the system can map events performed on unstructured data sources to events performed on structured data source. The system can then generate respective bot-RPAs for the tasks. The system can monitor, in real-time, data received from unstructured and/or structured data sources and when it identifies data for one of the bot-RPAs, automatically execute the RPA to perform the task.

Abstract: A domain-specific document is retrieved. The domain-specific document is related to the performance of a process in the domain and includes a plurality of document spans and a plurality of annotations that annotate the spans. A goal-based framework is generated from the domain-specific document. The generation is based on the plurality of annotations and based on the plurality of document spans. The goal-based framework includes entities and entity relationships related to performance of the process. The goal-based framework is transformed into a platform agnostic dialog model based on the entities and the entity relationships. At least one platform specific dialog model is provided to at least one automated response system based on the platform agnostic dialog model.

Abstract: A domain-specific document is retrieved. The domain-specific document is related to the performance of a process in the domain and includes a plurality of document spans and a plurality of annotations that annotate the spans. A goal-based framework is generated from the domain-specific document. The generation is based on the plurality of annotations and based on the plurality of document spans. The goal-based framework includes entities and entity relationships related to performance of the process. The goal-based framework is transformed into a platform agnostic dialog model based on the entities and the entity relationships. At least one platform specific dialog model is provided to at least one automated response system based on the platform agnostic dialog model.

Abstract: Methods, systems, and computer program products for generating dialog system workspaces are provided herein. A computer-implemented method includes obtaining (i) a set of policy documents and (ii) a set of initial questions; identifying at least one of the policy documents in the set of policy documents that is relevant to answering a given one of the initial questions in the set of initial questions; generating, based at least in part on an analysis of said identified policy document, (i) at least one follow-up question to said given initial question and (ii) two or more candidate answers to said at least one follow-up question; generating a dialog tree comprising at least (i) a parent node corresponding to the at least one follow-up question and (ii) child nodes corresponding to the two or more candidate answers; translating the dialog tree into a dialog workspace; and deploying the dialog workspace in an intelligent dialog system.

Abstract: One embodiment provides a method, including: obtaining a plurality of conversational logs; generating a human agent emulator and a user emulator; providing a workspace for a conversational agent, so that an agent designer generates a conversational specification for the conversational agent, wherein the generating a conversational specification comprises: receiving a selection, by the agent designer, of at least one intent for the conversational agent, wherein the receiving a selection is responsive to the conversational agent workspace providing suggestions for intents; providing at least one suggestion for a dialog node that corresponds to the selected at least one intent; and generating a dialog flow for the conversational agent, wherein the generating comprises iteratively receiving, from the agent designer, selection of at least one aspect and receiving at least one selection of the at least one suggestion for dialog nodes; and providing the conversational agent.

Abstract: Methods, systems, and computer program products in connection with a system for bidding on multi-modal sub-spaces for e-commerce searches is provided herein. A computer-implemented method includes representing, in an embedding space affiliated with at least one electronic commerce website: (i) images related to one or more bids and (ii) items of text related to the one or more bids, the images and the items of text both coming from one or more bidding entities. Such a method also includes mapping a query to the embedding space based on a comparison of features of the query to (i) the images and (ii) the items of text. Further, such a method additionally includes selecting at least one of the one or more bidding entities based on the mapping, and outputting, to at least one user in response to the query, search results associated with the at least one selected bidding entity.

Abstract: Methods, systems, and computer program products for generating metadata for image-based querying are provided herein. A computer-implemented method includes processing a query image against a database by applying a deep learning visual model to the query image and images contained within the database; retrieving a set of multiple images from the database based on the processing; identifying subsets of images among the set of multiple images by analyzing metadata attribute values of the set of multiple images and nearest neighbor distance values between the query image and the multiple images in the set; determining one or more items of metadata attributable to the query image by processing metadata of the subsets of images; and outputting, to a user, the items of metadata in response to the query image.

Abstract: One embodiment provides a method, including: obtaining a plurality of chatlogs involving a conversational agent, wherein the plurality of chatlogs represent interactions between users and the conversational agent; identifying dialog states of the conversational agent, wherein each dialog state corresponds to a possible response by the conversational agent; generating a dialog tree including one or more dialog flows of the conversational agent, wherein a dialog flow represents an interaction that occurred between the conversational agent and a user; and providing a conversational reflow user interface for a conversational reflow system allowing execution of the conversational agent to mimic interactions between users and the conversational agent, wherein the conversational reflow system advances through dialog flows based upon input provided by a user at the conversational reflow user interface and the conversational reflow user interface displays output based upon the dialog states of the dialog tree.

Abstract: Methods, systems, and computer program products for unsupervised learning of interpretable conversation models from conversation logs are provided herein. A computer-implemented method includes obtaining human-to-human conversation logs; training a deep learning model by (i) learning, in an unsupervised manner, semantic labels for dialog contexts in the multiple human-to-human conversation logs, (ii) mapping the learned semantic labels to query responses across the multiple human-to-human conversation logs, and (iii) inferring one or more entities from the multiple conversation logs based at least in part on the mapping; constructing a human-interpretable conversation model based at least in part on patterns determined via the trained deep learning model; and outputting the human-interpretable conversation model to at least one user.

Abstract: A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities.

Abstract: Methods, systems, and computer program products for generating metadata for image-based querying are provided herein. A computer-implemented method includes processing a query image against a database by applying a deep learning visual model to the query image and images contained within the database; retrieving a set of multiple images from the database based on the processing; identifying subsets of images among the set of multiple images by analyzing metadata attribute values of the set of multiple images and nearest neighbor distance values between the query image and the multiple images in the set; determining one or more items of metadata attributable to the query image by processing metadata of the subsets of images; and outputting, to a user, the items of metadata in response to the query image.

Abstract: One embodiment provides a method, including: obtaining a plurality of conversational logs; generating a human agent emulator and a user emulator; providing a workspace for a conversational agent, so that an agent designer generates a conversational specification for the conversational agent, wherein the generating a conversational specification comprises: receiving a selection, by the agent designer, of at least one intent for the conversational agent, wherein the receiving a selection is responsive to the conversational agent workspace providing suggestions for intents; providing at least one suggestion for a dialog node that corresponds to the selected at least one intent; and generating a dialog flow for the conversational agent, wherein the generating comprises iteratively receiving, from the agent designer, selection of at least one aspect and receiving at least one selection of the at least one suggestion for dialog nodes; and providing the conversational agent.

Abstract: A computer generates labels for machine learning algorithms by retrieving, from a data storage circuit, multiple label sets that contain labels that each classify data points in a corpus of data. A graph is generated that includes a plurality of edges, each edge between two respective labels from different label sets of the multiple label sets. Weights are determined for the plurality of edges based upon a consistency between data points classified by two labels connected by the edges. An algorithm is applied that groups labels from the multiple label sets based upon the weights for the plurality of edges. Data points are identified from the corpus of data that represent conflicts within the grouped labels. An electronic message is transmitted in order to present the identified data points to entities for further classification. A new label set is generated using the further classification received from the entities.