Abstract: Examples of the present disclosure provide systems and methods relating to a machine comprehension test with a learning-based approach, harnessing neural networks arranged in a parallel hierarchy. This parallel hierarchy enables the model to compare the passage, question, and answer from a variety of perspectives, as opposed to using a manually designed set of features. Perspectives may range from the word level to sentence fragments to sequences of sentences, and networks operate on word-embedding representations of text. A training methodology for small data is also provided.

Abstract: Examples of the present disclosure provide systems and methods relating to a machine comprehension test with a learning-based approach, harnessing neural networks arranged in a parallel hierarchy. This parallel hierarchy enables the model to compare the passage, question, and answer from a variety of perspectives, as opposed to using a manually designed set of features. Perspectives may range from the word level to sentence fragments to sequences of sentences, and networks operate on word-embedding representations of text. A training methodology for small data is also provided.

Abstract: A computer-implemented method, system using at least one computing device, and computer program product are disclosed for linking an ontology provided by a content service with a word expansion ontology. The content service ontology is referred to as a category ontology and the word expansion ontology is referred to herein as a lexical ontology. A user may provide an input such as an input command to an application. The input command is processed by a natural language processing engine to derive the user's intent and to extract relevant entities embodied in the command. The NLP engine may create a composite concept set containing multiple permutations of the concepts (entities extracted) and provide the composite concept set to a concept mapper. The concept mapper applies searches an ontology map and applies one or more scoring operations to determine a best match between the composite concept set and at least one category provided by the category ontology.

Abstract: A method, system, and computer program product provide a conversation agent to process natural language queries expressed by a user and perform commands according to the derived intention of the user. A natural language processing (NLP) engine derives intent using conditional random fields to identify a domain and at least one task embodied in the query. The NLP may further identify one or more subdomains, and one or more entities related to the identified command. A template system creates a data structure for information relevant to the derived intent and passes a template to a services manager for interfacing with one or more services capable of accomplishing the task. A dialogue manager may elicit more entities from the user if required by the services manager and otherwise engage in conversation with the user. In one embodiment, the conversational agent allows a user to engage in multiple conversations simultaneously.

Abstract: A server, method, and non-transitory computer readable medium for classifying a query into one of a plurality of classes are provided. The server includes a network interface, a memory storage unit and a processor. The method involves receiving a query applying a plurality of support vector machine models, calculating a probability, and determining a top class. The non-transitory computer readable medium is encoded with codes to direct a processor to carry out the method.

Abstract: A method, system, and computer program product provide a conversation agent to process natural language queries expressed by a user and perform commands according to the derived intention of the user. A natural language processing (NLP) engine derives intent using conditional random fields to identify a domain and at least one task embodied in the query. The NLP may further identify one or more subdomains, and one or more entities related to the identified command. A template system creates a data structure for information relevant to the derived intent and passes a template to a services manager for interfacing with one or more services capable of accomplishing the task. A dialog manager may elicit more entities from the user if required by the services manager and otherwise engage in conversation with the user. In one embodiment, the conversational agent allows a user to engage in multiple conversations simultaneously.

Abstract: Disclosed embodiments include systems and methods relevant to improvements to natural language processing used to determine an intent and one or more associated parameters from 5 a given input string. In an example, an input string is received and first and second different n-grams are applied to the input string. Recurrent neural network models are then used to generate output data based in part on the first and second different n-grams. In particular embodiments a recurrent neural network in both forward and backward directions specific to unigrams is applied. Intent detection and semantic labeling are applied to the output of the recurrent neural network models.

Abstract: A method and system are provided for processing natural language user queries for commanding a user interface to perform functions. Individual user queries are classified in accordance with the types of functions and a plurality of user queries may be related to define a particular command. To assist with classification, a query type for each user query is determined where the query type is one of a functional query requesting a particular new command to perform a particular type of function, an entity query relating to an entity associated with the particular new command having the particular type of function and a clarification query responding to a clarification question posed to clarify a prior user query having the particular type of function. Functional queries may be processed using a plurality of natural language processing techniques and scores from each technique combined to determine which type of function is commanded.

Abstract: A method and system are provided for processing natural language user queries for commanding a user interface to perform functions. Individual user queries are classified in accordance with the types of functions and a plurality of user queries may be related to define a particular command. To assist with classification, a query type for each user query is determined where the query type is one of a functional query requesting a particular new command to perform a particular type of function, an entity query relating to an entity associated with the particular new command having the particular type of function and a clarification query responding to a clarification question posed to clarify a prior user query having the particular type of function. Functional queries may be processed using a plurality of natural language processing techniques and scores from each technique combined to determine which type of function is commanded.

Abstract: Provided is a system, method and computer-readable medium for generating data that may be used to train models for a natural language processing application. A system architect creates a plurality of sentence patterns that include entity variables and initiates sentence generation. Each entity is associated with one or more entity data sources. A language generator accepts the sentence patterns as inputs, and references the various entity sources to create a plurality of generated sentences. The generated sentences may be associated with a particular class and therefore used to train one or more statistical classification models and entity extraction models for associated models. The sentence generated process may be initiated and controlled using a user interface displayable on a computing device, the user interface in communication with the language generator module.

Abstract: Described herein are systems and methods for providing a natural language generator in a spoken dialog system that considers both lexicalized and delexicalized dialog act slot-value pairs when translating one or more dialog act slot-value pairs into a natural language output. Each slot and value associated with the slot in a dialog act are represented as (dialog act+slot, value), where the first term (dialog act+slot) is delexicalized and the second term (value) is lexicalized. Each dialog act slot-value representation is processed to produce at least one delexicalized sentence as an output. A lexicalized sentence is produced by replacing each delexicalized slot with the value associated with the delexicalized slot.

Abstract: Methods and a natural language processor for processing a natural language query are provided. The processor includes a classifier, a rule-based pre-processor, a rule-based post-processor, a named entity recognizer, and an output module. The method involves receiving a text representation of the natural language query, pre-processing the text representation, applying a classification statistical model to the text representation when pre-processing fails, applying a post-processing rule, and performing name entity recognition.

Abstract: Provided is a system, method and computer-readable medium for generating data that may be used to train models for a natural language processing application. A system architect creates a plurality of sentence patterns that include entity variables and initiates sentence generation. Each entity is associated with one or more entity data sources. A language generator accepts the sentence patterns as inputs, and references the various entity sources to create a plurality of generated sentences. The generated sentences may be associated with a particular class and therefore used to train one or more statistical classification models and entity extraction models for associated models. The sentence generated process may be initiated and controlled using a user interface displayable on a computing device, the user interface in communication with the language generator module.

Abstract: A method, system, and computer program product provide a conversation agent to process natural language queries expressed by a user and perform commands according to the derived intention of the user. A natural language processing (NLP) engine derives intent using conditional random fields to identify a domain and at least one task embodied in the query. The NLP may further identify one or more subdomains, and one or more entities related to the identified command. A template system creates a data structure for information relevant to the derived intent and passes a template to a services manager for interfacing with one or more services capable of accomplishing the task. A dialogue manager may elicit more entities from the user if required by the services manager and otherwise engage in conversation with the user. In one embodiment, the conversational agent allows a user to engage in multiple conversations simultaneously.

Abstract: A method, system, and computer program product provide a conversation agent to process natural language queries expressed by a user and perform commands according to the derived intention of the user. A natural language processing (NLP) engine derives intent using conditional random fields to identify a domain and at least one task embodied in the query. The NLP may further identify one or more subdomains, and one or more entities related to the identified command. A template system creates a data structure for information relevant to the derived intent and passes a template to a services manager for interfacing with one or more services capable of accomplishing the task. A dialog manager may elicit more entities from the user if required by the services manager and otherwise engage in conversation with the user. In one embodiment, the conversational agent allows a user to engage in multiple conversations simultaneously.

Abstract: A computer-implemented method, system using at least one computing device, and computer program product are disclosed for linking an ontology provided by a content service with a word expansion ontology. The content service ontology is referred to as a category ontology and the word expansion ontology is referred to herein as a lexical ontology. A user may provide an input such as an input command to an application. The input command is processed by a natural language processing engine to derive the user's intent and to extract relevant entities embodied in the command. The NLP engine may create a composite concept set containing multiple permutations of the concepts (entities extracted) and provide the composite concept set to a concept mapper. The concept mapper applies searches an ontology map and applies one or more scoring operations to determine a best match between the composite concept set and at least one category provided by the category ontology.

Abstract: Described herein are systems and methods for providing a natural language comprehension system (NLCS) that iteratively performs an alternating search to gather information that may be used to predict the answer to the question. The NLCS first attends to a query glimpse of the question, and then finds one or more corresponding matches by attending to a text glimpse of the text.

Abstract: Described herein are systems and methods for providing a natural language generator in a spoken dialogue system that considers both lexicalized and delexicalized dialogue act slot-value pairs when translating one or more dialogue act slot-value pairs into a natural language output. Each slot and value associated with the slot in a dialogue act are represented as (dialogue act+slot, value), where the first term (dialogue act+slot) is delexicalized and the second term (value) is lexicalized. Each dialogue act slot-value representation is processed to produce to produce at least one delexicalized sentence as an output. A lexicalized sentence is produced by replacing each delexicalized slot with the value associated with the delexicalized slot.

Abstract: Described herein are systems and methods for providing a natural language comprehension system that employs a two-stage process for machine comprehension of text. The first stage indicates words in one or more text passages that potentially answer a question. The first stage outputs a set of candidate answers for the question, along with a first probability of correctness for each candidate answer. The second stage forms one or more hypotheses by inserting each candidate answer into the question and determines whether a sematic relationship exists between each hypothesis and each sentence in the text. The second processing circuitry generates a second probability of correctness for each candidate answer and combines the first probability with the second probability to produce a score that is used to rank the candidate answers. The candidate answer with the highest score is selected as a predicted answer.

Abstract: Described herein are systems and methods for two-stage training of a spoken dialogue system. The first stage trains a policy network using external data to produce a semi-trained policy network. The external data includes one or more known fixed dialogues. The second stage trains the semi-trained policy network through interaction to produce a trained policy network. The interaction may be interaction with a user simulator.