Abstract: An example system and method for selectively conveying content via User Interface (UI) display screen sections of Business Intelligence (BI) software and for facilitating user interaction therewith, e.g., so as to impart usefulness or preference ratings thereto, which can then affect subsequently displayed content. An example method includes providing one or more UI controls enabling a user to provide an input query and to interact with resulting content (i.e., query results, also called search results), where the user interaction with the content provides information (e.g., context information) indicative of a usefulness or preference rating pertaining to the content; thereby enabling modifications to subsequently retrieved content in accordance with the usefulness or preference rating(s). Such rating(s) may be aggregated from plural users of an associated software application.

Abstract: An automated system and method of converting legacy decision logic to a target format. The legacy files are received by the decision logic translation system, which outputs the business rule content in a standard rule structure, according to the selected target format. The process involves decision logic-based rule extraction. In general, methods or processes for extracting business rules have been difficult to reproduce and do not present clearly the extracted rules regarding the concepts of business rules, their composition and categorization. These drawbacks lead to incomplete extraction of rules and massive manual effort to achieve a complete extraction and verification. In contrast, the proposed system overcomes these drawbacks, and outputs files that can be easily used to migrate the business rules to a new platform.

Abstract: Systems and methods for using a generative artificial intelligence (AI) model using a multi-turn process to generate a suggested draft reply to a selected message. A first turn of the multi-turn process uses a shorter prompt including at least a portion of the body of the selected message and that requests multiple draft replies from the AI model. The resulting AI-generated draft replies are shortened, summarized, and/or otherwise converted into a plurality of shortened summaries that are presented as reply options to a user. Upon selecting a shortened summary, a more robust prompt is generated in a second turn with the AI model with the selected reply option to generate a more complex suggested draft reply to the selected message. Additionally, various customization options are provided, which when selected, reframe a query presented to the AI model to generate a more relevant and personalized response.

Abstract: The systems and methods described herein can generate a voice-based interface to increase the accuracy of translations. The voice-based interface can result in fewer input audio signals being transmitted between devices of a network. A method includes: receiving a first input audio signal; generating, based on the first input audio signal, a first translation string in a second language and a second translation string in a first language; determining a first translation score based on a likelihood that the first input audio signal includes an utterance in the first language and a second translation score based on a likelihood that the first input audio signal includes an utterance in the second language; selecting the first translation string based on the first translation score and the second translation score; generating an output signal from the first translation string; and transmitting the output signal to the client device.

Abstract: Certain aspects of the present disclosure provide techniques for orchestrating a user experience using natural language input. A user experience is orchestrated within an ecosystem of features for which a plurality of corresponding tokens is defined. Natural language describing a desired user experience result is received by a user experience orchestrator. A sequence of tokens corresponding to operations belonging to an ecosystem of features which produce a correct result for the natural language input can be identified by a trained large language model and executed by the user experience orchestrator using a token operator. The output operations determined by the model to produce or be likely to produce the correct result based on the natural language input can be disambiguated, confirmed, and/or executed.

Abstract: A computer-implemented method of processing language, includes: calculating a value indicating a degree of easiness of association between words with regard to one or more combinations of words in a first word group included in first text data and a second word group included in second text data; associating a first word and a second word based on the value indicating the degree of easiness of association; calculating a value indicating a degree of easiness of association between word groups with regard to a word group in the first text data including the first word and a word group in the second text data including the second word, using a result of the associating and the value indicating the degree of easiness of association between the words; and updating the value indicating the degree of easiness of association between the words by using the value indicating the degree of easiness of association between the word groups.

Abstract: Systems and methods for handling multilingual queries are provided. One example method includes receiving, at a computing device, an input, wherein the input comprises a multi-lingual query comprising at least a first source language and a second source language. The multi-lingual query is translated, word for word, into a destination language to produce a monolingual query, with the word order of the multilingual query and the word order of the monolingual query being the same. The monolingual query is processed using natural language processing to map the mono-lingual query to a natural language query in the destination language.

Abstract: Computer-readable media, systems and methods may improve automatic summarization of transcripts of financial earnings calls. For example, a system may generate segments, such as by disambiguating sentences, from a transcript to be summarized. The system may use an estimator that assesses whether or not the segment should be included in the summary. Different types of estimators may be used. For example, the estimator may be rule-based, trained based on machine-learning techniques, or trained on based on machine-learning with language modeling using natural language processing to fine-tune language models specific to financial earnings calls.

Abstract: A pharmacy management system includes a processor and a memory storing instructions that, when executed by processor, cause the pharmacy management system to train a machine learning model; receive a sig code utterance; analyze the sig code utterance using the trained machine learning model; and generate an output. A computer-implemented method includes training a machine learning model; receiving a sig code utterance; analyzing the sig code utterance using the trained machine learning model; and generating an output. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to train a machine learning model; receive a sig code utterance; analyze the sig code utterance using the trained machine learning model; and generate an output.

Abstract: Aspects of the invention include a computer-implemented method including extracting a domain-specific object feature from a first image data, wherein the feature describes an object in the first image data. A domain-specific semantic meaning of text data is determined. The object feature is mapped to a portion of the text data, wherein the portion of the text data describes the object. A joint representation of the object and the portion of the text data is created. A second image data and a query directed towards an object in the second image data is received. An answer to the query is generated based on the joint representation.

Abstract: Described herein are systems, methods, and computer readable media for dynamically determining a language variant to use for vehicle output to a vehicle occupant based on the vehicle's location. A geographic region may include multiple sub-regions, each of which may be associated with a respective one or more language variants. As an example, a geographic region may be a state or province, and each sub-region may have one or more dialects that are spoken by individuals in that sub-region. In some cases, a particular dialect may be predominant in a given sub-region. As a vehicle traverses a travel path, it may determine its current location, which geographic sub-region includes that location, and which language variant (e.g., dialect) is predominant there. That language variant may then be selected for in-vehicle communication with a vehicle occupant. The vehicle location determination may be made at or near where the occupant entered the vehicle.

Abstract: A content access device uses local audio translation for content presentation. The content access device receives video and first audio data associated with a first language. The content access device uses translation software and/or other automated translation services to translate the first audio data to second audio data associated with a second language. The content access device synchronizes the video with the second audio data and outputs the video and the second audio data for presentation. The first audio data may be audio, text, and so on. The second audio data may be output as audio, text, and so on.

Abstract: An embodiment of the present invention is directed to combining natural language processing with constrained grammar defined pattern matching to advantageously allow a system to translate natural language questions into any number of underlying technologies. By using a unique intermediate parse tree representation, the disclosed embodiments are able to instantiate a corresponding data store adapter for each given query, which may be for a relational database or a no-SQL database, for example. The ability to abstract underlying storage technology advantageously allows the management of disparate database systems, which is not possible using existing methods and technology.

Abstract: A method for extracting information from a document, comprising: receiving an identification of an entity to be extracted from the document; identifying candidates from the document, each candidate corresponding to a given element contained in the document and having a given location within the document; embedding the candidates, thereby obtaining an embedding vector for each candidate; for each candidate, comparing in a semantic space the respective embedding vector to previous embedding vectors associated with previous entity values previously chosen for the entity, thereby obtaining a first comparison result; for each candidate, comparing in a pixel space the given location within the document of the candidate to a location associated with the previous entity values previously chosen for the entity, thereby obtaining a second comparison result; sorting the candidates using the first and second comparison results obtained for each candidate, thereby obtaining sorted candidates; and outputting the sorted can

Abstract: Systems and methods for automatic evaluation of the quality of NLG outputs. In some aspects of the technology, a learned evaluation model may be pretrained first using NLG model pretraining tasks, and then with further pretraining tasks using automatically generated synthetic sentence pairs. In some cases, following pretraining, the evaluation model may be further fine-tuned using a set of human-graded sentence pairs, so that it learns to approximate the grades allocated by the human evaluators.

Abstract: An information processing apparatus according to an embodiment of the disclosure includes: a dictionary storage that stores headwords and dictionary contents associated with information corresponding to the headwords; an input device that inputs a character string in accordance with an input operation; and a processor that makes a question for learning the character string, based on a character string entered to the input device as a search target for a headword of the dictionary contents and an input operation of entering the character string to the input device.

Abstract: Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the bilateral contextual alignment of textual material and associated diagrammatic matter. The system, computer program product, and method disclosed herein facilitate leveraging a trained cognitive system to understand textual and diagrammatic patterns established by a user, and to learn to draft and edit such textual material and associated diagrammatic matter based on user behavior and preferences. The cognitive system is trained to bilaterally translate between textual material and diagrammatic matter, where the cognitive system includes natural language processing (NLP) features, diagram analytics features, and one or more user profiles.

Abstract: Provided are a model training method for neural machine translation that enhances an encoder using a monolingual corpus of a target language and improves the accuracy of the entire translator, and a machine translation system for performing the model training method. The machine translation system 1000 uses a monolingual corpus of the target language to obtain multiple pieces of pseudo source language data, thus allowing for obtaining a large amount of pseudo parallel corpus data having diversity. Further, the machine translation system 1000 uses both the pseudo parallel corpus data having diversity, which has been obtained in large quantities, and the base parallel corpus data in a small quantity but with high accuracy, with the applied learning rates changed accordingly, to perform the learning process (training process) for the machine translation model. This allows the machine translation system 1000 to obtain a learned model (machine translation model) with very high accuracy.

Abstract: Disclosed are method and apparatus for correcting voice dialogue, including: recognizing first text information of a dialogue speech input by a user, including a first semantic keyword determined from a plurality of candidate terms; feeding back a first result with the first semantic keyword to the user based on the first text information; feeding back the plurality of candidate terms to the user in response to the user's selection of the first semantic keyword from the first result; and receiving a second semantic keyword input by the user, correcting the first text information based on the second semantic keyword, determining corrected second text information, and feeding back a second result with the second semantic keyword to the user based on the second text information. The problem of true ambiguity can be solved, while improving the fault tolerance and processing capability of the dialogue apparatus for corresponding errors.

Abstract: A public speaking trainer has a computer system including a display monitor. A microphone is coupled to the computer system. A video capture device is coupled to the computer system. A biometric device is coupled to the computer system. A simulated environment including a simulated audience member is rendered on the display monitor using the computer system. A presentation is recorded onto the computer system using the microphone and video capture device. A first feature of the presentation is extracted based on data from the microphone and video capture device while recording the presentation. A metric is calculated based on the first feature. The simulated audience member is animated in response to a change in the metric. A score is generated based on the metric. The score is displayed on the display monitor of the computer system after recording the presentation. A training video is suggested based on the score.

Abstract: Provided are a method and an electronic device for editing text. The method includes receiving a first text from a user, wherein the first text is to be transmitted to a receiver; obtaining at least one second text that has been previously transmitted or received between the user and the receiver; identifying a first structure of the first text and a second structure of the obtained at least one second text, wherein the first structure and the second structure includes at least one of a paragraph structure and a sentence structure; and changing at least part of the first text by changing the first structure of the first text based on the second structure of the obtained at least one second text.

Abstract: Provided is a zero user interface (UI)-based automatic interpretation method including receiving a plurality of speech signals uttered by a plurality of users from a plurality of terminal devices, acquiring a plurality of speech energies from the plurality of received speech signals, determining main speech signal uttered in a current utterance turn among the plurality of speech signals by comparing the plurality of acquired speech energies, and transmitting an automatic interpretation result acquired by performing automatic interpretation on the determined main speech signal to the plurality of terminal devices.

Abstract: Systems and methods for integrating a messaging client application with a content management system are provided. In some examples, a method can include receiving messaging content at a content management system from a messaging client application via an application programming interface, the content management system and the messaging client application being on different cloud-based systems. The method can include associating the messaging content with a workspace in the content management system, the workspace including a plurality of users that are authorized to access the workspace to collaborate with each of the plurality of users. The method can further include providing access to the messaging content to the plurality of users that are authorized to access the workspace of the content management system. The method also can include embedding the messaging content in the workspace of the content management system.

Abstract: Techniques are described herein for translating source code in one programming language to source code in another programming language using machine learning. A method includes: receiving first source code in a first higher-level programming language; processing the first source code, or an intermediate representation thereof, using a sequence-to-sequence neural network model to generate a sequence of outputs, each including a probability distribution; generating second source code in a second higher-level programming language by, for each output in the sequence of outputs: determining a highest probability in the probability distribution associated with the output; in response to the highest probability exceeding a first threshold, generating a predicted portion of the second source code based on a token that corresponds to the highest probability; and in response to the highest probability not exceeding the first threshold, generating a placeholder; and outputting the second source code.

Abstract: A multilingual text-to-speech synthesis method and system are disclosed. The method includes receiving an articulatory feature of a speaker regarding a first language, receiving an input text of a second language, and generating output speech data for the input text of the second language that simulates the speaker's speech by inputting the input text of the second language and the articulatory feature of the speaker regarding the first language to a single artificial neural network multilingual text-to-speech synthesis model. The single artificial neural network multilingual text-to-speech synthesis model is generated by learning similarity information between phonemes of the first language and phonemes of the second language based on a first learning data of the first language and a second learning data of the second language.

Abstract: A system and method for improved categorization and sentiment analysis which is fed textual data such as transcriptions or collated data from a network enabled service, or some other source, which then segments textual data into chunks, parses the data chunks, and analyzes it using a plurality of techniques and metadata gathering methods to determine the sentiment of participating individuals concerning entities mentioned in the textual data and to categorize the discussions, for the purpose of taking actions to improve business outcomes.

Abstract: A server accesses a natural language query corresponding to one of a plurality of natural languages. The server maps, using a query-to-vector engine configured to leverage word embeddings in each of the plurality of natural languages to map natural language queries in the plurality of natural languages to vectors corresponding to meanings of the natural language queries, the natural language query to a vector. The server matches the vector to an intent representing a prediction associated with the natural language query. The server provides a response to the natural language query based on the intent.

Abstract: Provided are an interactive machine translation method and apparatus, a device, and a medium. The method includes: acquiring a source statement input by a user; translating the source statement into a first target statement; determining whether the user adjusts a first vocabulary in the first target statement; and in response to determining that the user adjusts the first vocabulary in the first target statement, acquiring a second vocabulary for replacing the first vocabulary, and adjusting, based on the second vocabulary, a vocabulary sequence located in a front of the first vocabulary and a vocabulary sequence located behind the first vocabulary in the first target statement to generate a second target statement.

Abstract: Embodiments provide for improved data sequence validity processing, for example to determine validity of sentences or other language within a particular language domain. Such improved processing is useful at least for arranging data sequences based on determined validity, and/or making determinations and/or performing actions based on the determined validity. A determined probability (e.g., transformed into the perplexity space) of each token appearing in a data sequence is used in any of a myriad of manners to perform such data sequence validity processing. Example embodiments provide for generating a perplexity value set for each data sequence in a plurality of data sequences, generating a probabilistic ranking set for the plurality of data sequences based on the perplexity value sets and at least one sequence ranking metric, and generating an arrangement of the plurality of data sequences based on the probabilistic ranking set.

Abstract: A computer based system and method for identifying complaint interactions, including: detecting appearances of linguistic structures related to complaints in an interaction; calculating at least one sentiment metric of the interaction; and classifying the interaction as being or not being a complaint interaction based on the detected linguistic structures and the at least one sentiment metric, for example using a trained supervised learning model.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media to automatically present suitable content for a particular locale. In some implementations, a computer-implemented method includes receiving gaming content associated with a game associated with a first client locale, the received gaming content including content that is restricted at a second client locale, receiving at least one content alternative, the at least one content alternative being an alternative to replace the received gaming content, generating a first localized rating for the received gaming content and a second localized rating for the at least one content alternative, and automatically providing the received gaming content or the at least one content alternative to a user device associated with the second client locale based on the first localized rating and the second localized rating.

Abstract: A policy management server manages a segmentation policy and policy constraints. The segmentation policy comprises a set of segmentation rules that each permit connections between specified groups of workloads that provide or consume network-based services. The policy constraints comprise a set of constraint rules that determine compliance of the segmentation rules. A workflow process may be initiated to resolve non-compliant rules by enabling an administrator to approve or deny the rule. In a large enterprise managing significant numbers of workloads, the policy constraints may be employed to ensure that overly permissive segmentation rules are not being created. This facilitates creation of a robust and narrowly tailored segmentation policy that reduces exposure of the enterprise to network-based security threats.

Abstract: A computerized method of representing customer interactions with an organization includes: receiving, by a computing device, customer web interaction data segments and customer conversation data segments; pre-processing the customer conversation data segments to remove specified types of information; scoring each of the pre-processed customer conversation data segments; pre-processing the customer web data interaction segments; extracting from the pre-processed customer web interaction data segments tokens; combining the pre-processed customer conversation data segments and the pre-processed customer web interaction data segments into a customer data set; parsing the customer data set into one or more windows; assigning, for each window, pre-trained weights to each of the tokens in each window; assigning a transaction theme to each window based on the tokens in each window; and generating, based on the transaction themes, a ranked list of topic keywords reflecting the customer web interaction data segments an

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for extracting text from an input document to generate one or more inference. Each inference box may be input into a machine learning network trained on training labels. Each training label provides a human-augmented version of output from a separate machine translation engine. A first translation may be generated by machine learning network. The first translation may be displayed in a user interface with respect to display of an original version of the input document and a translated version of a portion of the input document.

Abstract: A method and a system for using a graph neural network framework to implement a link prediction in a multiplex network environment is provided. The method includes: identifying a plurality of layers of a multiplex network, each respective layer including a respective plurality of nodes; for each node included in at least a first layer, providing, by a structural node label and determining a common embedding across all of the plurality of layers and an individual embedding for each individual layer; using a k-nearest approach to select a subset of the plurality of layers for performing link prediction with respect to each layer based on the determined embeddings; and performing a link prediction by determining a respective feed-forward network with respect to each layer included in the selected subset.

Abstract: A system is provided for determining which component of a speech processing system is the cause of an undesired response to a user input. The system processes ASR data and NLU data to determine the component likely to cause the undesired response. Based on which component is the cause of the undesired response, the system performs an appropriate conversation recovery technique to confirm the speech processing results with the user.

Abstract: A method of facilitating video monitoring of at least one location involves: causing a web application hosted by a local application on a local computing device to cause the local computing device to transcode input video data encoded in an input video encoding format to transcoded video data transcoded in a transcoded video encoding format supported for rendering by the local application, the input video data representing at least one video recording by at least one video recording device at the at least one location; and causing the web application to cause the local computing device to render the at least one video recording using the transcoded video data. Other methods, and computer-readable storage media and systems, are also disclosed.

Abstract: Systems and methods for profile-based language translation and filtration are provided. A user language profile specifying one or more translation rules may be stored in memory for a user. A current communication session associated with a user device of the user may be monitored. The current communication session may includes messages from one or more other user devices of one or more other users. A language set in at least one of the messages of the current communication session may be detected as triggering at least one of the translation rules in real-time. The language set in the at least one message may further be filtered in real-time based on the at least one translation rule, which may thereby modify the at least one message. Further, a presentation of the current communication session that is provided to the user device may be modified to include the filtered language set of the modified message instead of the triggering language set.

Abstract: A computing system can include one or more machine-learned models configured to receive context data that describes one or more entities to be named. In response to receipt of the context data, the machine-learned model(s) can generate output data that describes one or more names for the entity or entities described by the context data. The computing system can be configured to perform operations including inputting the context data into the machine-learned model(s). The operations can include receiving, as an output of the machine-learned model(s), the output data that describes the name(s) for the entity or entities described by the context data. The operations can include storing at least one name described by the output data.

Abstract: There is disclosed a method and system for translating a source phrase in a first language into a second language. The method being executable by a device configured to access an index comprising a set of source sentences in the first language, and a set of target sentences in the second language, each of the target sentence corresponding to a translation of a given source sentence. The method comprises: acquiring the source phrase; generating by a translation algorithm, one or more target phrases, each of the one or more target phrases having a different semantic meaning within the second language; retrieving, from the index, a respective target sentence for each of the one or more target phrases, the respective target sentence comprising one of the one or more target phrases; and selecting each of the one or more target phrase and the respective target sentences for display.

Abstract: A method implemented by a processor of a computing device, comprising: receiving an image from a camera; using a machine vision process to recognize the at least one real-world object in the image; displaying on a screen an augmented reality (AR) scene containing the at least one real-world object and a virtual agent; receiving user input; deriving a simplified user intent from the user input; and in response to the user input, animating the virtual agent within the AR scene, the animating being dependent on the simplified user intent. Deriving a simplified user intent from the user input may include converting the user input into a user phrase, determining at least one semantic element in the user phrase, and converting the at least one semantic element into the simplified user intent.

Abstract: In an approach to determining the effectiveness of a proposed solution, one or more computer processors monitor real-time communications. The one or more computer processors identify or more topics associated with the monitored real-time communications. The one or more computer processors feed the identified one or more topics and associated real-time communications into a solution efficacy model. The one or more computer processors generate based on one or more calculations by the solution efficacy model, an efficacy rating for the identified real-time communications. The one or more computer processors generate a prioritization of the identified real-time communications based on the generated efficacy rating.

Abstract: An electronic device is provided. The electronic device includes a memory configured to store a speech translation model and at least one processor electronically connected with the memory. The at least one processor is configured to train the speech translation model based on first information related to conversion between a speech in a first language and a text corresponding to the speech in the first language, and second information related to conversion between a text in the first language and a text in a second language corresponding to the text in the first language, and the speech translation model is trained to convert a speech in the first language into a text in the second language and output the text.

Abstract: Systems and methods for automatic evaluation of the quality of NLG outputs. In some aspects of the technology, a learned evaluation model may be pretrained first using NLG model pretraining tasks, and then with further pretraining tasks using automatically generated synthetic sentence pairs. In some cases, following pretraining, the evaluation model may be further fine-tuned using a set of human-graded sentence pairs, so that it learns to approximate the grades allocated by the human evaluators.

Abstract: An apparatus includes at least one processor to, in response to a request to perform speech-to-text conversion: perform a pause detection technique including analyzing speech audio to identify pauses, and analyzing lengths of the pauses to identify likely sentence pauses; perform a speaker diarization technique including dividing the speech audio into fragments, analyzing vocal characteristics of speech sounds of each fragment to identify a speaker of a set of speakers, and identifying instances of a change in speakers between each temporally consecutive pair of fragments to identify likely speaker changes; and perform speech-to-text operations including dividing the speech audio into segments based on at least the likely sentence pauses and likely speaker changes, using at least an acoustic model with each segment to identify likely speech sounds in the speech audio, and generating a transcript of the speech audio based at least on the likely speech sounds.

Abstract: A pharmacy management system for automated sig code translation using machine learning includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the pharmacy management system to train a machine learning model to analyze sig codes, receive a sig code, analyze the sig code utterance and generate an output corresponding to the sig code utterance. A computer-implemented method includes training a machine learning model to analyze sig codes, receiving a sig code, analyzing the sig code utterance, and generating an output corresponding to the sig code utterance. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to: train a machine learning model to analyze sig codes, receive a sig code, analyze the sig code utterance, and generate an output corresponding to the sig code utterance.

Abstract: An auxiliary device charging case is used to facilitate translation features of a mobile computing device or auxiliary device. A first user, who may be a foreign language speaker, holds the charging case and speaks into the charging case. The charging case communicates the received speech to the mobile computing device, either directly or through the auxiliary device, which translates the received speech into a second language for a second user, who is the owner of the mobile computing device and auxiliary device. The second user may provide input in the second language, such as by speaking or typing into the auxiliary or mobile computing device. The mobile computing device may translate this second input to the first language, and transmit the translated input to the charging case either directly or through the auxiliary device. The charging case may output the translated second input to the first user, such as through a speaker or display screen.

Abstract: A machine accesses a query in a first natural language. The machine identifies an event corresponding to the query. The machine computes, using a cross-lingual information retrieval module, a ranked list of documents in a second natural language that are related to the event. At least a portion of documents in the ranked list are selected from a collection of documents in the second natural language that are not annotated with events. The cross-lingual information retrieval module is trained using a dataset comprising annotated documents in the first natural language and translations of the annotated documents into the second natural language. Each annotated document is annotated with one or more events. The machine provides an output representing at least a portion of the ranked list of documents in the second natural language. The second natural language is different from the first natural language.

Abstract: Provided are methods, systems, and devices for generating semantic objects and an output based on the detection or recognition of the state of an environment that includes objects. State data, based in part on sensor output, can be received from one or more sensors that detect a state of an environment including objects. Based in part on the state data, semantic objects are generated. The semantic objects can correspond to the objects and include a set of attributes. Based in part on the set of attributes of the semantic objects, one or more operating modes, associated with the semantic objects can be determined. Based in part on the one or more operating modes, object outputs associated with the semantic objects can be generated. The object outputs can include one or more visual indications or one or more audio indications.

Abstract: The accuracy of machine translation is increased. A translated document with high translation accuracy is obtained. An original document is faithfully translated. An original document is translated with a neural network to generate a first translated document; a modification-target word or phrase is determined from words and phrases contained in the original document on the basis of an analysis result for the first translated document; the modification-target word or phrase is replaced with a high frequency word in learning data used for learning in the neural network to modify the original document; and the modified original document is translated with the neural network to generate a second translated document.