Patent ID: 11886812
Assignee: GRAMMARLY, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method comprising:
receiving under digital program control, by a digital model, electronic digital data representing a first text sequence in a first language, wherein the digital model comprises a fluency-adjusted grammatical error correction model;
training the digital model to generate grammatically corrected and fluency-adjusted text sequences using an encoder-decoder neural network with an attention mechanism and at least one long term short term memory (LSTM) unit, including first training on domain-independent training data that comprises a set of uncorrected text items and for an uncorrected text item, a corresponding corrected text item, from text sequences of a plurality of different topics and writing styles received from by native and non-native speakers of various native backgrounds and second fine-tuning training on in-domain training data that comprises a set of text sequences and, for a text sequence, a set of corresponding features, the set of corresponding features comprising a proficiency label and a native language label, wherein at least one type of error is present in the first text sequence and the location of the error within the first text sequence for the in-domain training data;
the digital model comprising a plurality of artificial neural network layers and model parameters associated with the artificial neural network layers, a value of a particular model parameter indicative of a relationship between the proficiency label, or a proficiency label-native language label combination, and the text sequence, and the corresponding corrected text item;
the digital model having been fine-tuned, after having been machine-learned, using the in-domain data set comprising text sequences that have been labeled with native languages and proficiency levels to only adjust a subset of the values of the model parameters associated with an encoding layer or an embedding layer or both the encoding layer and the embedding layer, the subset comprising one or more of a number of epochs, batch size, learning rate, and start decay;
using the digital model, modifying the first text sequence to result in creating and digitally storing a second text sequence in the first language, the modifying comprising any one or more of: deleting text from the first text sequence, the modifying comprising grammatical correction and fluency adjustment of the first text sequence based on a particular native language and proficiency level; adding text to the first text sequence; modifying text of the first text sequence; reordering text of the first text sequence; adding a digital markup to the first text sequence; and
outputting, by the digital model, the second text sequence in the first language.