Abstract: A method includes providing input text to a plurality of multi-task learning (MTL) models corresponding to a plurality of domains. Each MTL model is trained to generate an embedding vector based on the input text. The method further includes providing the input text to a domain identifier that is trained to generate a weight vector based on the input text. The weight vector indicates a classification weight for each domain of the plurality of domains. The method further includes scaling each embedding vector based on a corresponding classification weight of the weight vector to generate a plurality of scaled embedding vectors, generating a feature vector based on the plurality of scaled embedding vectors, and providing the feature vector to an intent classifier that is trained to generate, based on the feature vector, an intent classification result associated with the input text.

Abstract: An online version of a sentence representation generation module updated by training a first sentence representation generation module using first labeled data of a first corpus. After training the first sentence representation generation module using the first labeled data, a second corpus of second labeled data is obtained. The second corpus is distinct from the first corpus. A subset of the first labeled data is identified based on similarities between the first corpus and the second corpus. A second sentence representation generation module is trained using the second labeled data of the second corpus and the subset of the first labeled data.

Abstract: A computer-implemented method includes obtaining a training data set including a plurality of training examples. The method includes generating, for each training example, multiple feature vectors corresponding, respectively, to multiple feature types. The method includes applying weighting factors to feature vectors corresponding to a subset of the feature types. The weighting factors are determined based on one or more of: a number of training examples, a number of classes associated with the training data set, an average number of training examples per class, a language of the training data set, a vocabulary size of the training data set, or a commonality of the vocabulary with a public corpus. The method includes concatenating the feature vectors of a particular training example to form an input vector and providing the input vector as training data to a machine-learning intent classification model to train the model to determine intent based on text input.

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, system and computer program product are provided for implementing dynamic confidence rescaling for modularity in automatic user intent detection systems. User intents are identified using separately trained models with corresponding training data. Natural language processing (NLP) and statistical analysis are applied on the training data to classify the training data into groups and modules. A confidence rescaling algorithm is used for combining the modules. The dynamic confidence rescaling uses statistical information computed about each module being combined to identify user intents with enhanced accuracies in comparison to baseline models without confidence rescaling.

Abstract: Methods, systems, computer-readable media, and apparatuses for providing search disambiguation using contextual information and domain ontologies are presented. In some embodiments, a computing device may receive a natural language input from a user. The computing device may identify a plurality of hypotheses for the natural language input. The computing device may map the plurality of hypotheses to one or more concepts of a plurality of concepts of an ontology by annotating the one or more concepts. The ontology may include the plurality of concepts respectively connected by a plurality of relations. The computing device may determine that there is an imperfect match between the annotated one or more concepts and annotations of answers. In response, the computing device may disambiguate the annotated one or more concepts using the ontology. The computing device may present output to the user based on the disambiguation.

Abstract: Aspects described herein provide solutions to problems posed by a user. Input that includes a specified subject may be received from a user. A specified descriptor for the specified subject may be obtained from a hierarchical taxonomy associated with the specified subject. An information repository may be searched based on the specific descriptor, and entries stored at the information repository that are associated with the specific descriptor may be indicated in a list of results. The specific descriptor may be iteratively generalized to obtain generalized descriptors, and the information repository may be searched based on the generalized descriptors. The generalized descriptors may also be specialized to obtain specialized descriptors, and the information repository may be searched based on the specialized descriptors. The list of results may include entries stored at the information repository that are respectively associated with the generalized descriptors and the specialized descriptors.

Abstract: A computer-implemented method for building a semantic analysis model. In one embodiment, the computer-implemented method includes creating proxy tags comprising a set of surface form variants. The computer-implemented method creates training examples comprising a combination of terminal tokens and at least one of the proxy tags. The computer-implemented method builds the semantic analysis model using the training examples.

Abstract: Methods and apparatus to receive a first data stream, such as a public broadcast, and receive a second data stream, such as a private data stream, containing emails, for example. A user profile can be used to generate a data output stream for the user from the first and second data streams. The user profile can contain preferences for segments within the first and second data streams.

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: 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: Methods, devices, and computer program products for recognizing and responding to natural language input are described herein. Natural language input is received at a natural language input interface of a computing device and transformed into computer-usable text. A natural language input recognizer obtains evidence from one or more evidence source and generates an evidence graph based on the evidence obtained. Evidence may be obtained asynchronously, and the natural language input recognizer may update the evidence graph upon receipt of additional evidence. The natural language input recognizer generates a set of recognition hypotheses based on the evidence graph and selects one of the recognition hypotheses as a recognition result for the natural language input. Semantic models, evidence models, and response models may be employed to generate the evidence graph and respond to the recognition result selected for the natural language input.

Abstract: Methods and apparatus to receive a first data stream, such as a public broadcast, and receive a second data stream, such as a private data stream, containing emails, for example. A user profile can be used to generate a data output stream for the user from the first and second data streams. The user profile can contain preferences for segments within the first and second data streams.

Abstract: According to some aspects, a method of searching for content in response to a user voice query is provided. The method may comprise receiving the user voice query, performing speech recognition to generate N best speech recognition results comprising a first speech recognition result, performing a supervised search of at least one content repository to identify one or more supervised search results using one or more classifiers that classify the first speech recognition result into at least one class that identifies previously classified content in the at least one content repository, performing an unsupervised search of the at least one content repository to identify one or more unsupervised search results, wherein performing the unsupervised search comprises performing a word search of the at least one content repository, and generating combined results from among the one or more supervised search results and the one or more unsupervised search results.

Abstract: Aspects described herein provide solutions to problems posed by a user. Input that includes a specified subject may be received from a user. A specified descriptor for the specified subject may be obtained from a hierarchical taxonomy associated with the specified subject. An information repository may be searched based on the specific descriptor, and entries stored at the information repository that are associated with the specific descriptor may be indicated in a list of results. The specific descriptor may be iteratively generalized to obtain generalized descriptors, and the information repository may be searched based on the generalized descriptors. The generalized descriptors may also be specialized to obtain specialized descriptors, and the information repository may be searched based on the specialized descriptors. The list of results may include entries stored at the information repository that are respectively associated with the generalized descriptors and the specialized descriptors.

Abstract: Methods, devices, and computer program products for recognizing and responding to natural language input are described herein. Natural language input is received at a natural language input interface of a computing device and transformed into computer-usable text. A natural language input recognizer obtains evidence from one or more evidence source and generates an evidence graph based on the evidence obtained. Evidence may be obtained asynchronously, and the natural language input recognizer may update the evidence graph upon receipt of additional evidence. The natural language input recognizer generates a set of recognition hypotheses based on the evidence graph and selects one of the recognition hypotheses as a recognition result for the natural language input. Semantic models, evidence models, and response models may be employed to generate the evidence graph and respond to the recognition result selected for the natural language input.

Abstract: Techniques disclosed herein include systems and methods for managing user interface responses to user input including spoken queries and commands. This includes providing incremental user interface (UI) response based on multiple recognition results about user input that are received with different delays. Such techniques include providing an initial response to a user at an early time, before remote recognition results are available. Systems herein can respond incrementally by initiating an initial UI response based on first recognition results, and then modify the initial UI response after receiving secondary recognition results. Since an initial response begins immediately, instead of waiting for results from all recognizers, it reduces the perceived delay by the user before complete results get rendered to the user.

Abstract: Methods, systems, computer-readable media, and apparatuses for providing search disambiguation using contextual information and domain ontologies are presented. In some embodiments, a computing device may receive a natural language input from a user. The computing device may identify a plurality of hypotheses for the natural language input. The computing device may map the plurality of hypotheses to one or more concepts of a plurality of concepts of an ontology by annotating the one or more concepts. The ontology may include the plurality of concepts respectively connected by a plurality of relations. The computing device may determine that there is an imperfect match between the annotated one or more concepts and annotations of answers. In response, the computing device may disambiguate the annotated one or more concepts using the ontology. The computing device may present output to the user based on the disambiguation.

Abstract: A natural language query arrangement is described for a mobile environment. An automatic speech recognition (ASR) engine can process an unknown speech input from a user to produce corresponding recognition text. A natural language understanding module can extract natural language concept information classifier uses the from the recognition text. A query recognition text and the natural language concept information to assign to the speech input a query intent related to one or more objects in the mobile environment. An environment database contains information descriptive of objects in the mobile environment. A query search engine searches the environment database based on the query intent, the natural language concept information, and the recognition text to determine corresponding search results, which can be to the user.

Abstract: Techniques disclosed herein include systems and methods for managing user interface responses to user input including spoken queries and commands. This includes providing incremental user interface (UI) response based on multiple recognition results about user input that are received with different delays. Such techniques include providing an initial response to a user at an early time, before remote recognition results are available. Systems herein can respond incrementally by initiating an initial UI response based on first recognition results, and then modify the initial UI response after receiving secondary recognition results. Since an initial response begins immediately, instead of waiting for results from all recognizers, it reduces the perceived delay by the user before complete results get rendered to the user.