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: Embodiments provide a computer implemented method, in a data processing system including a processor and a memory including instructions which are executed by the processor to cause the processor to train an enhanced chatflow system, the method including: ingesting a corpus of information including at least one user input node corresponding to a user question and at least one variation for each user input node; for each user input node: designating the node as a class; storing the node in a dialog node repository; designating each of the at least one variations as training examples for the designated class; converting the classes and the training examples into feature vector representations; training one or more training classifiers using the one or more feature vector representations of the classes; and training classification objectives using the one or more feature vector representations of the training examples.

Abstract: A system and method performs automated domain concept discovery and clustering using word embeddings by receiving a set of documents for natural language processing for a domain, representing a plurality of entries in the set of documents as continuous vectors in a high dimensional continuous space, applying a clustering algorithm based on a mutual information optimization criterion to form a set of clusters, associating each entry of the plurality of entries with each cluster in the set of clusters through formalizing an evidence based model of each cluster given each entry, calculating a mutual information metric between each entry and each cluster using the evidence based model, and identifying a nominal center of each cluster by maximizing the mutual information.

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.

Abstract: A method, apparatus and computer program product for presenting a user interface for a conversational system is described. A unified semantic representation of conversational content between a user and conversational system is created as a contextual graph of concepts and relations. A set of subgraph components of the semantic contextual graph dynamically identified based on a current dialog activity. The identified set of subgraph components in a user interface as a set of graphical elements representing respective concepts and relations.

Abstract: A method, apparatus and computer program product for creating a dialog system for web content is described. Knowledge is extracted from a target web application for the dialog system. The knowledge includes an organizational structure of the target web application and domain knowledge pertinent to the target web application. A deep learning process associates the domain knowledge with the organization structure of the target application. A plurality of knowledge sources of different respective types are created from the domain knowledge and the organizational structure. Each of the knowledge sources is used for providing answers to user queries to the dialog system. As part of the invention, a semantic matcher is provided to select among the answers provided by the plurality of knowledge sources for a best answer to a user query.

Abstract: Embodiments provide a computer implemented method of training an enhanced chatflow system, comprising: ingesting a corpus of information comprising at least one user input node corresponding to a user question and at least one expert-designed variation for each user input node; matching one or more user inputs to one or more corresponding dialog nodes using regular expressions and delimiters; ingesting one or more usage logs from a deployed dialog system, each usage log comprising at least one user input node; for each user input node: designating the node as a class; storing the node in a dialog node repository; designating each of the at least one variations as training examples for the designated class; converting the classes and the training examples into feature vector representations; training one or more classifiers and one or more classification objectives using the feature vector representations.

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, 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.

Abstract: A method, apparatus and computer program product for creating a dialog system for web content is described. Knowledge is extracted from a target web application for the dialog system. The knowledge includes an organizational structure of the target web application and domain knowledge pertinent to the target web application. A deep learning process associates the domain knowledge with the organization structure of the target application. A plurality of knowledge sources of different respective types are created from the domain knowledge and the organizational structure. Each of the knowledge sources is used for providing answers to user queries to the dialog system. As part of the invention, a semantic matcher is provided to select among the answers provided by the plurality of knowledge sources for a best answer to a user query.

Abstract: A method, apparatus and computer program product for presenting a user interface for a conversational system is described. A unified semantic representation of conversational content between a user and conversational system is created as a contextual graph of concepts and relations. A set of subgraph components of the semantic contextual graph dynamically identified based on a current dialog activity. The identified set of subgraph components in a user interface as a set of graphical elements representing respective concepts and relations.

Abstract: A method, apparatus and computer program product for presenting a user interface for a conversational system is described. 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. A first sentential concept graph is consolidated into a unified contextual graph. The unified contextual graph is updated based on new sentential concept graphs while the dialog with the conversational system progresses.

Abstract: A system and method performs automated domain concept discovery and clustering using word embeddings by receiving a set of documents for natural language processing for a domain, representing a plurality of entries in the set of documents as continuous vectors in a high dimensional continuous space, applying a clustering algorithm based on a mutual information optimization criterion to form a set of clusters, associating each entry of the plurality of entries with each cluster in the set of clusters through formalizing an evidence based model of each cluster given each entry, calculating a mutual information metric between each entry and each cluster using the evidence based model, and identifying a nominal center of each cluster by maximizing the mutual information.

Abstract: An interactive communication system configured to conduct a call with a caller. The interactive communication system comprises at least one computer hardware processor configured to perform: obtaining a plurality of dialog chunks comprising information provided by the caller to the interactive communication system and information provided by the interactive communication system to the caller; generating, based on the plurality of dialog chunks, a respective plurality of feature sets, each of the plurality of feature sets comprising at least one feature generated using a respective dialog chunk of the plurality of dialog chunks; determining, based on the plurality of feature sets, a respective plurality of dialog chunk scores; determining, based at least in part on the plurality of dialog chunk scores, a likelihood that the caller is dissatisfied with the interactive communication system; and when the likelihood exceeds a threshold, performing a remedial action that alters how the call is handled.

Abstract: Embodiments provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to train an enhanced chatflow system, the method comprising: ingesting a corpus of information comprising at least one user input node corresponding to a user question and at least one variation for each user input node; for each user input node: designating the node as a class; storing the node in a dialog node repository; designating each of the at least one variations as training examples for the designated class; converting the classes and the training examples into feature vector representations; training one or more training classifiers using the one or more feature vector representations of the classes; and training classification objectives using the one or more feature vector representations of the training examples.

Abstract: Embodiments provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to train an enhanced chatflow system, the method comprising: ingesting, using a rule-based module, a corpus of information comprising at least one user input node corresponding to a user question and at least one expert-designed variation for each user input node; matching, using the rule-based module, one or more user inputs to one or more corresponding dialog nodes using regular expressions and delimiters; ingesting, using a statistical matching module, one or more usage logs from a deployed dialog system, each usage log comprising at least one user input node; for each user input node: designating the node as a class; storing the node in a dialog node repository; designating each of the at least one variations as training examples for the designated class; converting the classes and the training examples into fe

Abstract: An interactive communication system configured to conduct a call with a caller. The interactive communication system comprises at least one computer hardware processor configured to perform: obtaining a plurality of dialog chunks comprising information provided by the caller to the interactive communication system and information provided by the interactive communication system to the caller; generating, based on the plurality of dialog chunks, a respective plurality of feature sets, each of the plurality of feature sets comprising at least one feature generated using a respective dialog chunk of the plurality of dialog chunks; determining, based on the plurality of feature sets, a respective plurality of dialog chunk scores; determining, based at least in part on the plurality of dialog chunk scores, a likelihood that the caller is dissatisfied with the interactive communication system; and when the likelihood exceeds a threshold, performing a remedial action that alters how the call is handled.

Abstract: Aspects described herein provide various approaches to annotating text samples in order to construct natural language grammars. A text sample may be selected for annotation. A set of annotation candidates may be generated based on the text sample. A classifier may be used to score the set of annotation candidates in order to obtain a set of annotation scores. One of the annotation candidates may be selected as a suggested annotation for the text sample based on the set of annotation scores. A grammar rule may be derived based on the suggested annotation, and a grammar may be configured to include the annotation-derived grammar rule.

Abstract: Designing a dialog application is a difficult task that typically requires a complete understanding of the dialog framework and a high level of expertise to map system requirements to the actual implementations. In contrast, determining the logic of the dialog application via sample interaction is typically very simple and efficient. A developer can describe via speech or text what the operations of the application are, effectively writing dialog samples. Methods described herein reverse the way dialog applications are designed by obtaining annotated dialog samples and defined concepts related to a requested dialog application; analyzing the annotated dialog samples, defined concepts, and one or more relationships between or among the defined concepts; and generating an executable dialog application based on the analysis of the annotated dialog samples and the defined concepts.

Abstract: Aspects described herein provide various approaches to annotating text samples in order to construct natural language grammars. A text sample may be selected for annotation. A set of annotation candidates may be generated based on the text sample. A classifier may be used to score the set of annotation candidates in order to obtain a set of annotation scores. One of the annotation candidates may be selected as a suggested annotation for the text sample based on the set of annotation scores. A grammar rule may be derived based on the suggested annotation, and a grammar may be configured to include the annotation-derived grammar rule.