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: An example system includes a processor to receive unlabeled data, few-shot training data, and a pre-trained model. The processor can split the unlabeled data into a number of groups corresponding to different perspectives. The processor can generate weakly labeled data for each of the number of groups using a respective associated heuristic. The processor can inter-train a model for each different perspective based on respective weakly labeled data. The processor can fine-tune each inter-trained model based on the few-shot training data for each different perspective to generate a final model for each different perspective.

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: 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: In an approach to improve abstract summarization with question and answer rewards embodiments generate, by a question and answer generator, questions and answers corresponding to a generated summary. Further, embodiments evaluate received answers for the generated questions by analyzing received answers associated with the generated summary against answers received for an original summary, and calculate a reward based on the similarity between answers associated with generated summary and the original summary. Additionally, embodiments update the generation model by applying the calculated reward to further train the summary generation model.

Abstract: Summarization of customer service dialogs by: receiving, as input, a two-party multi-turn dialog; applying a trained next response prediction (NRP) machine learning model to the received dialog, to determine a level of significance of each utterance in the dialog with respect to performing an NRP task over the dialog; assigning a score to each of the utterances in the dialog, based, at least in part, on the determined level of significance; and selecting one or more of the utterances for inclusion in an extractive summarization of the dialog, based, at least in part, on the assigned scores.

Abstract: Systems and techniques that facilitate document generation by applying deep learning to conversation transcripts are provided. In various embodiments, a system can comprise a receiver component that can access a set of conversation transcripts. In various aspects, the system can further comprise a document component that can generate, via execution of one or more neural networks, a structured document based on the set of conversation transcripts. More specifically, the document component can generate a set of document chapters by executing a first neural network respectively on the set of conversation transcripts. The document component can further group, via execution of a clustering algorithm, the set of document chapters into one or more clusters of document chapters. The document component can then generate the structured document by executing a second neural network on a first cluster of document chapters from the one or more clusters of document chapters.

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: 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: One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text 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: Embodiments relate to a system, program product, and method directed at natural language (NL) and a virtual dialog platform. An NL statement is detected and analyzed to identify one or more entities expressed in the statement. The identified entities are leveraged to parse the statement into keywords. The intent of the received statement is represented as a relationship between two or more of the keywords. A knowledge representation is identified to represent the statement with respect to a formatted module having two or more components and a component relationship structure. Each statement keyword is assigned to a designated module component based on an alignment of the component relationship with the keyword relationship. The statement intent is expressed based on the relationship between the keywords, and a statement response is inferred. The inferred statement is communicated to the virtual dialog platform.

Abstract: One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text representation.

Abstract: Embodiments relate to a system, program product, and method directed at NL and a virtual dialog platform. A natural language (NL) statement is detected and analyzed to identify one or more entities expressed in the statement. The identified entities are leveraged to parse the statement into keywords. The intent of the received statement is represented as a relationship between two or more keywords. A knowledge representation is identified to represent the statement with respect to a formatted a module having two or more components and a component relationship structure. Each statement keyword is assigned to a designated module component based on an alignment of the component relationship with the keyword relationship. The statement intent is expressed based on the relationship between the keywords, and a statement response is inferred. The inferred statement is communicated to the virtual dialog platform.