Abstract: A system receives one or more transcripts of communications between entities. The system identifies a requested action in the communications based at least in part on a mapping between the requested action and an application programming interface. The system identifies one or more statements eliciting information, based on parameters to the application programming interface. The system generates a definition of an artificial agent based, at least in part, on the requested action and the one more statements eliciting information.

Abstract: Methods, systems, and computer-readable media for event extraction from documents with co-reference are disclosed. An event extraction service identifies one or more trigger groups in a document comprising text. An individual one of the trigger groups comprises one or more textual references to an occurrence of an event. The one or more trigger groups are associated with one or more semantic roles for entities. The event extraction service identifies one or more entity groups in the document. An individual one of the entity groups comprises one or more textual references to a real-world object. The event extraction service assigns one or more of the entity groups to one or more of the semantic roles. The event extraction service generates an output indicating the one or more trigger groups and one or more entity groups assigned to the semantic roles.

Abstract: Various embodiments of a hierarchical system or method of identifying sensitive content in data is described. In some embodiments, sensitive data classifiers local to a data storage system can analyze a plurality of data items and classify at least some data items as potentially containing sensitive data. The sensitive data classifiers can provide the classified data items to a separate sensitive data discovery component. The sensitive data discovery component can, in some embodiments, obtain the classified data items, perform a sensitive data location analysis on the classified data items to identify a location of sensitive data within some of the classified data items, and generate location information for the sensitive data within the data items containing sensitive data. The sensitive data discovery component can provide to a destination this information, in some embodiments, where the destination might redact, tokenize, highlight, or perform other actions on the located sensitive data.

Abstract: Techniques for performing natural language processing (NLP) on semi-structured data are described. An exemplary method includes receiving a semi-structured document to perform NLP on using a trained NLP model; converting the semi-structured document into a secondary format, wherein the secondary format includes spatial information for tokens of the semi-structured document; flattening the converted, secondary formatted semi-structured document into a Unicode Transformation Format text file; performing NLP on the Unicode Transformation Format text file using the trained NLP model; and providing a result of the NLP to a requester.

Abstract: A translation system receives examples of translations between a first language and a second language. In response to receiving request to translate a source text from the first language to the second language, the system ranks the examples based on the example's applicability to one or more portions of the source text. The system performs additional training of a neural network that was pre-trained to translate from the first language to the second language, where the additional training is based on one or more top-ranking examples. The system translates the source text to the second language using the additionally trained neural network.

Abstract: Techniques for creating a text classifier machine learning (ML) model are described. According to some embodiments, a language processing service finetunes a language ML model on unlabeled documents of a user, and then trains that finetuned language ML model on labeled documents of the user to be a text classifier that is customized for that user’s domain, e.g., the user’s documents. Additionally, the finetuned language ML model may be trained on labeled documents of the user, for prediction objectives for unlabeled data, before being trained as the text classifier.

Abstract: Techniques for data lake-based text generation and data augmentation for machine learning training are described. A user-provided dataset including documents and corresponding label information can be automatically supplemented by creating additional high-quality document samples, with labels, via a large repository of documents in a data lake. Documents from the data lake may be identified as being semantically similar to the user-provided documents but different enough to allow a resulting model to learn from the variation in these documents. New documents can be generated from user-provided document samples or data lake sample documents by identifying and replacing slots within the samples and rewriting adjunct tokens.

Abstract: Techniques for the generation of dubbed audio for an audio/video are described.

Abstract: Techniques for named-entity recognition are described. An exemplary implementation of a method includes extracting character features for each word of the document using a first encoder; extracting word level representations of for each word position using a second encoder, the word level representations being a concatenation of spelling variants; classifying the word level representations according to a first decoder; and outputting the classifications as named-entity labels.

Abstract: Methods, systems, and computer-readable media for event extraction from documents with co-reference are disclosed. An event extraction service identifies one or more trigger groups in a document comprising text. An individual one of the trigger groups comprises one or more textual references to an occurrence of an event. The one or more trigger groups are associated with one or more semantic roles for entities. The event extraction service identifies one or more entity groups in the document. An individual one of the entity groups comprises one or more textual references to a real-world object. The event extraction service assigns one or more of the entity groups to one or more of the semantic roles. The event extraction service generates an output indicating the one or more trigger groups and one or more entity groups assigned to the semantic roles.

Abstract: Methods, systems, and computer-readable media for context-sensitive linking of entities to private databases are disclosed. An entity linking service stores a plurality of representations of entities. Individual ones of the entities correspond to individual ones of a plurality of records in one or more private databases. The entity linking service determines a mention of an entity in a document. The entity linking service selects, from the plurality of records in the one or more private databases, a record corresponding to the entity. The record is selected based at least in part on the plurality of representations of the entities and based at least in part on a context of the mention of the entity in the document. The entity linking service generates output comprising a reference to the selected record in the one or more private databases.

Abstract: Methods, systems, and computer-readable media for lifecycle management for customized natural language processing are disclosed. A natural language processing (NLP) customization service determines a task definition associated with an NLP model based (at least in part) on user input. The task definition comprises an indication of one or more tasks to be implemented using the NLP model and one or more requirements associated with use of the NLP model. The service determines the NLP model based (at least in part) on the task definition. The service trains the NLP model. The NLP model is used to perform inference for a plurality of input documents. The inference outputs a plurality of predictions based (at least in part) on the input documents. Inference data is collected based (at least in part) on the inference. The service generates a retrained NLP model based (at least in part) on the inference data.

Abstract: A method, computer program product and system are provided. The method includes the steps of: providing output text and a confidence value and displaying the output text. The output text includes text in a second language produced from machine translation performed on input text in a first language. The confidence value corresponds to an indication of reliability of the output text. The display of the output text includes a display attribute corresponding to the confidence value of the output text.

Abstract: Translating named entities from a source language to a target language. In general, in one implementation, the technique includes: generating potential translations of a named entity from a source language to a target language using a pronunciation-based and spelling-based transliteration model, searching a monolingual resource in the target language for information relating to usage frequency, and providing output including at least one of the potential translations based on the usage frequency information.

Abstract: A method, computer program product and system are provided. The method includes the steps of: providing output text and a confidence value and displaying the output text. The output text includes text in a second language produced from machine translation performed on input text in a first language. The confidence value corresponds to an indication of reliability of the output text. The display of the output text includes a display attribute corresponding to the confidence value of the output text.

Abstract: Translating named entities from a source language to a target language. In general, in one implementation, the technique includes: generating potential translations of a named entity from a source language to a target language using a pronunciation-based and spelling-based transliteration model, searching a monolingual resource in the target language for information relating to usage frequency, and providing output including at least one of the potential translations based on the usage frequency information.

Abstract: Translating named entities from a source language to a target language. In general, in one implementation, the technique includes: generating potential translations of a named entity from a source language to a target language using a pronunciation-based and spelling-based transliteration model, searching a monolingual resource in the target language for information relating to usage frequency, and providing output including at least one of the potential translations based on the usage frequency information.

Abstract: Translating named entities from a source language to a target language. In general, in one implementation, the technique includes: generating potential translations of a named entity from a source language to a target language using a pronunciation-based and spelling-based transliteration model, searching a monolingual resource in the target language for information relating to usage frequency, and providing output including at least one of the potential translations based on the usage frequency information.