Abstract: A plurality of constraints associated with conversational steps implemented by a conversation model is extracted from the conversation model. Using the conversational steps and the constraints, a directed graph is constructed, each node in the directed graph representing a conversational step, each directed edge in the directed graph representing a possible execution path from a first conversational step to a second conversational step. An edge in the graph is populated with flow data denoting a probability associated with the edge. By traversing a portion of the graph, an experience preview is generated, the experience preview demonstrating a user experience of a portion of the conversation model.

Abstract: In an approach for a cooperative build and content annotation system for conversational design of virtual assistants, a processor formulates a build context based on a build activity of a user. A processor formulates one or more content queries based on the build context. A processor builds a content index by augmenting a text-search index with a neural Information Retrieval (IR) index. A processor searches the content index using the one or more content queries to identify content relevant to the build context. A processor determines at least one recommendation for the user based on heuristic rules applied to the build context and the identified content, wherein each recommendation is a build suggestion or a content annotation suggestion.

Abstract: In an approach for a cooperative build and content annotation system for conversational design of virtual assistants, a processor formulates a build context based on a build activity of a user. A processor formulates one or more content queries based on the build context. A processor builds a content index by augmenting a text-search index with a neural Information Retrieval (IR) index. A processor searches the content index using the one or more content queries to identify content relevant to the build context. A processor determines at least one recommendation for the user based on heuristic rules applied to the build context and the identified content, wherein each recommendation is a build suggestion or a content annotation suggestion.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include generating a content page and selecting an annotation container for the content page. The operations may include configuring the annotation container and inputting content into the annotation container. The operations may include submitting at least one attribute to the annotation container to associate the attribute with the content. The operations may include including the content and the at least one attribute in container data and extracting a model from the container data. The operations may include importing the model into a dialog skill and embedding the dialog skill into a user interface.

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: 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 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: One embodiment provides a method, including: receiving an input from a first user requesting information; generating a conversation model from a dialog that occurs between the user and a human agent; recording the human agent performing an external action required to respond to the input; mapping steps performed during performance of the external action to conversation slots within the dialog; generating an integrated interpretable conversation model comprising a dialog and action script; receiving, at a conversational agent system, a subsequent input from a second user requesting similar information to the information requested by the first user; and providing, by the conversational agent system, a response to the subsequent input, wherein the providing a response comprises the conversational agent system utilizing the integrated interpretable conversational model to replay (i) the dialog and (ii) the action script using the subsequent input.

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: One embodiment provides a method, including: receiving an input from a first user requesting information; generating a conversation model from a dialog that occurs between the user and a human agent; recording the human agent performing an external action required to respond to the input; mapping steps performed during performance of the external action to conversation slots within the dialog; generating an integrated interpretable conversation model comprising a dialog and action script; receiving, at a conversational agent system, a subsequent input from a second user requesting similar information to the information requested by the first user; and providing, by the conversational agent system, a response to the subsequent input, wherein the providing a response comprises the conversational agent system utilizing the integrated interpretable conversational model to replay (i) the dialog and (ii) the action script using the subsequent input.

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: Provided herein is a system, method, and computer program product for modifying a language conversation model of the language learning system. Modifying the language conversation model includes receiving, using a conversational sub-system, voice inputs. The conversational sub-system converts the voice inputs to voice input data and processes the voice input data. The conversational sub-system detects an error in processing the voice input data and, based at least in part on the error, stores additional data comprising additional voice input data in a memory. The conversational sub-system applies machine learning to the additional data to derive a function that is not enabled within the language conversation model. The conversational sub-system develops an update that enables the language conversation model to implement the function. The update is applied to the language conversation model.

Abstract: Methods, systems, and computer program products for integrated curriculum based math problem generation are provided herein. A computer-implemented method includes generating a first map that represents a plurality of concepts pertaining to at least one subject matter area within a curriculum; generating a second map that represents a plurality of concepts pertaining to math word problems associated with the curriculum; determining portions of the first graph that exhibit a structural match to portions of the second graph; automatically creating a math word problem template by extracting multiple variable portions from one of the one or more math word problems associated with the curriculum; and automatically generating an integrated curriculum math word problem by replacing the multiple extracted variable portions in the template with concepts and relationships derived from the portions of the first graph that exhibit a structural match to the portions of the second graph.

Abstract: A computer receives a conversational input. The computer detects, based on the conversational input, relevant entities and relevant entity values, wherein the relevant entities and relevant entity values correspond to entities and entity values extracted from a catalog. The computer identifies, based on the relevant entities and relevant entity values, one or more matching products or services. The computer displays the one or more matching products or services. The computer assesses one or more attributes of the matching products or services for refinement. The computer provides one or more attribute refinement options based on the assessed one or more attributes. The computer receives user refinement in response to providing the one or more attribute refinement options. The computer receives a product or service selection.

Abstract: Process management techniques using a representation state transfer architecture include, for example, a method of managing a given process in a data-centric manner in a client/server environment, comprising at least one client and at least one server. In an exemplary method, the server obtains from the client at least one request associated with the given process. In response to the request obtained from the client, the server generates an entity, wherein the generated entity is represented in terms of a data model and a lifecycle model associated therewith, wherein the lifecycle model comprises states and state transitions with each transition caused by one or more activities in the given process, and wherein the given process is represented as a collection of communicating entities. The server sends a response to the at least one request to the client, wherein the response is based at least in part on the generated entity.

Abstract: Methods and arrangements for providing classification for a business process hierarchy. Packaged application objects are received, comprising at least one of the standard application objects and custom objects. At least one of the standard application objects is mapped to at least one node of a business process hierarchy, thereby forming at least one mapping template. At least one of the standard application objects is mapped to at least one of the custom objects to create a first mapping, and at least one of the custom objects is mapped to at least one of the standard application objects to create a second mapping. The at least one mapping template, the first mapping and the second mapping are combined, to create a mapping of at least one of the custom objects to the business process hierarchy. Other variants and embodiments are broadly contemplated herein.

Abstract: Provided herein is a system, method, and computer program product for modifying a language conversation model of the language learning system. Modifying the language conversation model includes receiving, using a conversational sub-system, voice inputs. The conversational sub-system converts the voice inputs to voice input data and processes the voice input data. The conversational sub-system detects an error in processing the voice input data and, based at least in part on the error, stores additional data comprising additional voice input data in a memory. The conversational sub-system applies machine learning to the additional data to derive a function that is not enabled within the language conversation model. The conversational sub-system develops an update that enables the language conversation model to implement the function. The update is applied to the language conversation model.

Abstract: One embodiment provides a method for generating a process learning graph and a document output from a recorded process for training a cognitive agent, the method comprising: utilizing at least one processor to execute computer code that performs the steps of: obtaining a recording of a process, wherein the recording comprises a demonstration of executing the process; generating, using the recording, the process learning graph, wherein the process learning graph identifies a process flow; generating, using the recording, the document output, wherein the document output comprises process screen transitions and process steps; and providing the process learning graph and the document output to the cognitive agent. Other aspects are described and claimed.

Abstract: One embodiment provides a method for generating a process learning graph and a document output from a recorded process for training a cognitive agent, the method comprising: utilizing at least one processor to execute computer code that performs the steps of: obtaining a recording of a process, wherein the recording comprises a demonstration of executing the process; generating, using the recording, the process learning graph, wherein the process learning graph identifies a process flow; generating, using the recording, the document output, wherein the document output comprises process screen transitions and process steps; and providing the process learning graph and the document output to the cognitive agent. Other aspects are described and claimed.