Abstract: A method executed by a computing device includes determining to update an entigen group within a knowledge database, where the entigen group includes a set of linked entigens that represent knowledge associated with an object. The method further includes determining a set of identigens for each word of a phrase associated with the object to produce sets of identigens. The method further includes interpreting the sets of identigens to determine a most likely meaning interpretation of the phrase and produce a new entigen group. The method further includes updating the entigen group utilizing the new entigen group to produce a curated entigen group such that a deficiency of the entigen group is resolved providing more accurate and more complete knowledge of the object.

Abstract: A system and method for automatically generating organization level ontology for knowledge retrieval, are provided. An input/output unit receives a plurality of documents from document sources and an ontology generation system generates the organization level ontology based on the documents. The ontology generation system extracts one or more nodes and directed relationships from each document and generates an intermediate document ontology for each document. A combination of syntactic, semantic, and pragmatic assessment of intermediate document ontology is performed to assess at least structure and adaptability of the ontology. The ontology generation system further generates a refined document ontology, based on assessment, to satisfy one or more quality metrics. Each of the refined document ontologies is integrated together to generate the organization level ontology.

Abstract: Systems, methods, apparatuses, and computer program products for natural language processing are provided. One method may include utilizing a trained machine learning model to learn syntax dependency patterns and parts of speech tag patterns of text based on labeled training data. The method may also include contextualizing vector embeddings from a language model for each word in the text, and extracting relationships for a given fragment of the text based on the contextualization. The method may further include resolving relationships between identified verbs based on a plurality of heuristics to identify the syntax dependency patterns, identifying nested relationships, and capturing metadata associated with the nested relationships.

Abstract: Using a logistic regression classification model executing on a processor, a topic is classified into an interaction type in a set of predefined interaction types. A set of documents corresponding to the topic is extracted from a document repository. Using a generative adversarial model executing on a processor, a sentiment corresponding to a reaction to a previous presentation is scored, the scoring resulting in a scored sentiment. Using a trained attention layer model, the interaction type, the set of documents, and the scored sentiment are weighted, the weighting generating a weighted interaction type, a weighted set of documents, and a weighted scored sentiment. Using a natural language generation transformer model executing on the processor, a document in the weighted set of documents is weighted according to the weighted interaction type and the weighted scored sentiment.

Abstract: In some embodiments, a processor receives natural language data for performing an identified cybersecurity task. The processor can provide the natural language data to a first machine learning (ML) model. The first ML model can automatically infer a template query based on the natural language data. The processor can receive user input indicating a finalized query and to provide the finalized query as input to a system configured to perform the identified computational task. The processor can provide the finalized query as a reference phrase to a second ML model, the second ML model configured to generate a set of natural language phrases similar to the reference phrase. The processor can generate supplemental training data using the set of natural language phrases similar to the reference phrase to augment training data used to improve performance of the first ML model and/or the second ML model.

Abstract: At least one processor may obtain a document comprising text tokens. The at least one processor may determine, based on a pre-trained language model, word embeddings corresponding to the text tokens. The at least one processor may determine, based on the word embeddings, named entities corresponding to the text tokens; and one or more accuracy predictions corresponding to the named entities. The at least one processor may compare the one or more accuracy predictions with at least one threshold. The at least one processor may associate, based on the comparing, the named entities with one or more confidence levels. The at last one processor may deliver the named entities and the one or more confidence levels.

Abstract: The present disclosure provides a dialog method and system, an electronic device and a storage medium, and relates to the field of artificial intelligence (AI) technologies such as deep learning and natural language processing. A specific implementation scheme involves: rewriting a corresponding dialog state based on received dialog information of a user; determining to-be-used dialog action information based on the dialog information of the user and the dialog state; and generating a reply statement based on the dialog information of the user and the dialog action information. According to the present disclosure, the to-be-used dialog action information can be determined based on the dialog information of the user and the dialog state; and then the reply statement is generated based on the dialog action information, thereby providing an efficient dialog scheme.

Abstract: A computer-implemented method for measuring cognitive load of a user creating a creative work in a creative work system, may include generating at least one verbal statement capable of provoking at least one verbal response from the user, prompting the user to vocally interact with the creative work system by vocalizing the at least one generated verbal statement to the user via an audio interface of the creative work system, and obtaining the at least one verbal response from the user via the audio interface, and determining the cognitive load of the user based on the at least one verbal response obtained from the user, wherein generating the at least one verbal statement is based on at least one predicted verbal response suitable for determining the cognitive load of the user.

Abstract: A speech recognition method includes: obtaining speech data; performing feature extraction on speech data, to obtain speech features of at least two speech segments; inputting the speech features of the at least two speech segments into the speech recognition model, and processing the speech features of the speech segments by using cascaded hidden layers in the speech recognition model, to obtain hidden layer features of the speech segments, a hidden layer feature of an ith speech segment being determined based on speech features of n speech segments located after the ith speech segment in a time sequence and a speech feature of the ith speech segment; and obtaining text information corresponding to the speech data based on the hidden layer features of the speech segments.

Abstract: A system and method for analyzing a response to an interview questions is disclosed, including a speech recognition engine to receive audio information corresponding to a response to create a transcription of the response. A segmentation engine segments the transcription into one or more segments. A segment classification engine classifies the one or more segments into one or more functional units and group the functional units by at least one structure. A presentation skill KPI engine, a structure sequence KPI engine, and a content KPI engine to analyze the response and applying the analysis to a composite model to provide an overall rating of the response.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for speech recognition. One method includes obtaining an input acoustic sequence, the input acoustic sequence representing an utterance, and the input acoustic sequence comprising a respective acoustic feature representation at each of a first number of time steps; processing the input acoustic sequence using a first neural network to convert the input acoustic sequence into an alternative representation for the input acoustic sequence; processing the alternative representation for the input acoustic sequence using an attention-based Recurrent Neural Network (RNN) to generate, for each position in an output sequence order, a set of substring scores that includes a respective substring score for each substring in a set of substrings; and generating a sequence of substrings that represent a transcription of the utterance.

Abstract: Disclosed embodiments may include a system that may receive an audio file comprising an interaction between a first user and a second user. The system may detect, using a deep neural network (DNN), moment(s) of interruption between the first and second users from the audio file. The system may extract, using the DNN, vocal feature(s) from the moment(s) of interruption. The system may determine, using a machine learning model (MLM) and based on the vocal feature(s), whether a threshold number of moments of the moment(s) of interruption corresponds to a first emotion type. When the threshold number of moments corresponds to the first emotion type, the system may transmit a first message comprising a first binary indication. When the threshold number of moments do not correspond to the first emotion type, the system may transmit a second message comprising a second binary indication.

Abstract: A system and method for masking an identity of a speaker of natural language speech, such as speech clips to be labeled by humans in a system generating voice transcriptions for training an automatic speech recognition model. The natural language speech is morphed prior to being presented to the human for labeling. In one embodiment, morphing comprises pitch shifting the speech randomly either up or down, then frequency shifting the speech, then pitch shifting the speech in a direction opposite the first pitch shift. Labeling the morphed speech comprises at least one or more of transcribing the morphed speech, identifying a gender of the speaker, identifying an accent of the speaker, and identifying a noise type of the morphed speech.

Abstract: A system capable of speech gap modulation is configured to: receive at least one composite speech portion, which comprises at least one speech portion and at least one dynamic-gap portion, wherein the speech portion(s) comprising at least one variable-value speech portion, wherein the dynamic-gap portion(s) associated with a pause in speech; receive at least one synchronization point, wherein synchronization point(s) is associating a point in time in the composite speech portion(s) and a point in time in other media portion(s); and modulate dynamic-gap portion(s), based at least partially on the at variable-value speech portion(s), and on the point(s), thereby generating at least one modulated composite speech portion. This facilitates improved synchronization of the modulated composite speech portion(s) and the other media portion(s) at the synchronization point(s), when combining the other media portion(s) and the audio-format modulated composite speech portion(s) into a synchronized multimedia output.

Abstract: The disclosure includes a system and method for receiving, using one or more processors, a case; mapping, using the one or more processors, a first label to one or more reportable activities in one or more jurisdictions, the first label associated with the case; prepopulating, using the one or more processors, one or more reports, the one or more reports reporting the one or more activities in the one or more jurisdictions; generating, using the one or more processors, a template-based narrative, wherein the template is based on the first label, the first label associated with a first category of activities; generating, using the one or more processors, a natural language narrative by applying natural language generation associated with the first category of activities to the template-based narrative; and prepopulating, using the one or more processors, a form field with the natural language narrative.

Abstract: To provide a sentence data management method and a sentence data management system in which sentence data and a provision of a regulation are associated and a risk of legal violation associated with outbound transmission of the sentence data is reduced. A sentence data management system includes a processor configured to convert a word string of a first document described using a first term into a word string described using a third term; convert a word string of an outbound-transmitted second document described using a second term into a word string described using the third term; compare the first document and the second document by using a comparison result between the word string and the word string as a comparison result between the word string and the word string; and check a possibility of violation to the regulation in outbound transmission to determine the outbound transmission of the second document.

Abstract: A system and method for authenticating an identity may include generating a first generic representation representing a stored audio content, generating a second generic representation representing input audio content, and, providing the first and second generic representations to a voice biometrics unit adapted to authenticate an identity based on the first and second generic representations.

Abstract: A system for proactively extracting data from complex documents is disclosed. The system may include one or more processors, an NLP device, a trained machine learning device, and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to, receive one or more documents from a client device, extract one or more extractable data entries from the one or more data entries, generate, one or more normalized data entries, and proactively generate and add one or more completed data entries in place of one or more placeholders in a first document template. The system may receive a natural language prompt from a user device and determine a machine-readable semantic representation. The system may identify sensitive data entries and generate a graphical user interface identifying completed data entries and associated confidence intervals.

Abstract: Techniques are disclosed for training and/or utilizing an automatic post-editing model in correcting translation error(s) introduced by a neural machine translation model. The automatic post-editing model can be trained using automatically generated training instances. A training instance is automatically generated by processing text in a first language using a neural machine translation model to generate text in a second language. The text in the second language is processed using a neural machine translation model to generate training text in the first language. A training instance can include the text in the first language as well as the training text in the first language.

Abstract: FIG. 1 illustrates a decoder for decoding a current frame to reconstruct an audio signal according to an embodiment. The audio signal is encoded within the current frame. The current frame includes a current bitstream payload. The current bitstream payload includes a plurality of payload bits. The plurality of payload bits encodes a plurality of spectral lines of a spectrum of the audio signal. Each of the payload bits exhibits a position within the current bitstream payload. The decoder includes a decoding module and an output interface. The decoding module is configured to reconstruct the audio signal. The output interface is configured to output the audio signal.