Abstract: A processor may transcribe an electronic representation of a dialogue. The processor may identify one or more question-answer pairs from the electronic representation. The processor may generate based upon the one or more identified question-answer pairs a summary of the dialogue. The processor may display the summary of the dialogue to a user.

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: According to one embodiment, a method, computer system, and computer program product for solution guided generation of responses for dialog systems is provided. The embodiment may include receiving, by a processor, first voice data associated with a first user utterance in conversation in a guided dialog system. The embodiment may include identifying from the first voice data a first topic of a set of topics associated with the first user utterance. The embodiment may include identifying 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 embodiment may include generating a first response for a second user based on a first solution segment of the first solution and the first voice data.

Abstract: Provided is a computer-implemented method, system, and computer program product for generating a goal-oriented dialogue from a grounding document. A processor may analyze a corpus of text. The processor may identify, based on the analyzing, one or more semantic structures that can be used to simulate a dialogue. The processor may generate, based on the identifying, a simulated dialogue, the simulated dialogue including one or more utterances from a simulated agent and one or more utterances from a simulated user to form a dialogue flow.

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: A processor may transcribe an electronic representation of a dialogue. The processor may identify one or more question-answer pairs from the electronic representation. The processor may generate based upon the one or more identified question-answer pairs a summary of the dialogue. The processor may display the summary of the dialogue to a user.

Abstract: A method, computer system, and a computer program for conducting a conversational search. In one embodiment, the method includes monitoring a dialogue involving at least one user and capturing user utterances provided during the dialogue. These user utterances are then analyzed and classified according to the context of the dialogue. The dialogue is intervened upon the determination that a user needs additional information and/or upon execution of an action on behalf of the user and based on the plurality of user utterances and context. The Required information may be provided back to the user using Documentation Recommendation Module. The Documentation Recommendation Module determines a valid resource recommendation as determined by a combination of the context and a resource that includes additional information.

Abstract: A method, apparatus and computer program product for presenting a user interface for a conversational system is described. A unified contextual graph for use by the conversational system, the unified contextual graph comprising components based on database queries of the conversational system and a user dialog between a user and the conversational system. For each of a set of user utterances produced in a dialog with the conversational system, a semantic meaning representation is determined. The semantic meaning representations are converted to respective sentential concept graphs. The unified contextual graph is updated based on new sentential concept graphs while the dialog with the conversational system progresses.

Abstract: A method, computer system, and a computer program product for natural language processing are provided. A first text corpus that includes semi-structured content that includes hierarchical nodes may be received. Some of the hierarchical nodes may be masked. Node embeddings and level embeddings may be generated from the semi-structured content of the first text corpus and from the masked hierarchical nodes. The node embeddings and the level embeddings may be included in a bi-directional transformer model. The bi-directional transformer model may be trained on the first text corpus by reducing loss from the bi-directional transformer model predicting the masked hierarchical nodes.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate end to end integration of dialogue history for spoken language understanding are provided. According to an embodiment, a system can comprise a processor that executes components stored in memory. The computer executable components comprise a conversation component that encodes speech-based content of an utterance and text-based content of the utterance into a uniform representation.

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: Methods, systems, and computer program products for context-based response generation are provided herein. A method includes: obtaining conversation logs comprising agent responses matched to contexts and a set of designated responses that are not matched to the contexts; replacing at least a portion of the agent responses with the designated responses to form modified conversation logs; training a first model, using the modified conversation logs, to output a designated response in the set for a given context and a second model, using the historical conversation logs, to output one of the agent responses for a given context; identifying one or more new responses based at least in part on the output of the second machine learning model for a particular one of the contexts; and retraining the first machine learning model based at least in part on the one or more new responses.

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: 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: A method, computer system, and a computer program product for natural language processing are provided. A first text corpus that includes semi-structured content that includes hierarchical nodes may be received. Some of the hierarchical nodes may be masked. Node embeddings and level embeddings may be generated from the semi-structured content of the first text corpus and from the masked hierarchical nodes. The node embeddings and the level embeddings may be included in a bi-directional transformer model. The bi-directional transformer model may be trained on the first text corpus by reducing loss from the bi-directional transformer model predicting the masked hierarchical nodes.

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 method for generating a context-aware knowledge base is provided. The method may include extracting document object model (DOM) tag elements associated with one or more webpages. The method may further include identifying and extracting webpage data associated with the extracted DOM tags. The method may further include determining a context associated with the identified and extracted webpage data by detecting and extracting resource description framework (RDF) triplets in candidate DOM tag elements. The method may further include ranking the extracted RDF triplets. The method may also include validating one or more RDF triplets associated with the ranked RDF triplets. The method may further include connecting the validated RDF triplets to a knowledge graph associated with a knowledge base of the one or more webpages.

Abstract: A method, apparatus and computer program product for presenting a user interface for a conversational system is described. A user input is received in a dialog between a user and the conversational system, the user input in a natural language. A domain trained semantic matcher is used to determine a set of entities and a user intent from the user input. One or more queries is generated to selected ones of a plurality of knowledge sources, the knowledge sources created from domain specific knowledge. The results from the one or more queries are ranked based on domain specific knowledge. A system response is presented in the dialog based on at least a highest ranked result from the plurality of knowledge sources.