Disclosed herein are example embodiments of an improved narrative generation system where an analysis service that executes data analysis logic that supports story generation is segregated from an authoring service that executes authoring logic for story generation through an interface. Accordingly, when the authoring service needs analysis from the analysis service, it can invoke the analysis service through the interface. By exposing the analysis service to the authoring service through the shared interface, the details of the logic underlying the analysis service are shielded from the authoring service (and vice versa where the details of the authoring service are shielded from the analysis service). Through parameterization of operating variables, the analysis service can thus be designed as a generalized data analysis service that can operate in a number of different content verticals with respect to a variety of different story types.

A generative automatic text summarization system and method is disclosed that may adopt a search and reranking strategy to improve the performance of a summarization task. The system and method may employ a transformer neural model to assist with the summarization task. The transformer neural model may be trained to learn human abstracts and may then be operable to generate abstractive summaries. With multiple summary hypothesis generated, a best-first search algorithm and reranking algorithm may be employed to select the best candidate summary as part of the output summary.

A generative automatic text summarization system and method is disclosed that may adopt a search and reranking strategy to improve the performance of a summarization task. The system and method may employ a transformer neural model to assist with the summarization task. The transformer neural model may be trained to learn human abstracts and may then be operable to generate abstractive summaries. With multiple summary hypothesis generated, a best-first search algorithm and reranking algorithm may be employed to select the best candidate summary as part of the output summary.

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.

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.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to effectively and efficiently generate one or more abstractive summaries of one or more multi-section documents. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an abstractive summary of a multi-section document comprising one or more sections, by generating one or more section summaries, section input batches for each selected section, model outputs created by one or more text summarization machine learning models through the performance of a batch processing operation sequence, abstractive summaries, and then storing the abstractive summaries.

Exemplary systems and methods for creating spatial contents in a mixed reality environment are disclosed. In an example, a location associated with a first user in a coordinate space is determined. A persistent virtual content is generated. The persistent virtual content is associated with the first user's associated location. The first user's associated location is determined and is associated with the persistent virtual content. A location of a second user at a second time in the coordinate space is determined. The persistent virtual content is presented to the second user via a display at a location in the coordinate space corresponding to the first user's associated location.

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. In some cases, following fine-tuning, the learned evaluation model may be distilled into a student model.

Technology is described herein for generating questions using a neural network. The technology generates the questions in a three-step process. In the first step, the technology selects, using a first neural network, a subset of textual passages from an identified electronic document. In the second step, the technology generates, using a second neural network, one or more candidate answers for each textual passage selected by the first neural network, to produce a plurality of candidate passage-answer pairs. In the third step, the technology selects, using a third neural network, a subset of the plurality of candidate passage-answer pairs. The technology then generates an output result that includes one or more output questions chosen from the candidate passage-answer pairs produced by the third neural network. The use of the first neural network reduces the processing burden placed on the second and third neural networks. It also reduces latency.

A method, computer program product, and computing system for receiving audio-based content from a user who is reviewing an image on a display screen; receiving gaze information that defines a gaze location of the user; and temporally aligning the audio-based content and the gaze information to form location-based content.