Grammarly, Inc.

Amazon Technologies, Inc.

Maria Nadejde

Joel Tetreault

Georgiana Dinu

Eric J Nowell

Aakash Upadhyay

Anna Currey

William Jew Wong

Margo Helena Lynch

Advait Bhatwadekar

Tatyana Badeka

Prajwal Gadad

Jenyuan Wang

Shuoren Fu

A respective label set is obtained for a number of translation unit pairs, with each pair comprising a set of language elements in a first language and the translation of the set of language elements to a second language. The label set includes values of one or more translation customization attributes. A value of such an attribute associated with a translation request is identified. A translated version of an input set of language elements indicated in the translation request is generated in accordance with the value of the attribute, using a machine learning model trained with the help of the label sets.

Customized machine translation using client-specified attributes

In an embodiment, the disclosed technologies are capable of receiving, by a digital model, data representing a first text sequence in a first language; using the digital model, modifying the first text sequence to result in creating and digitally storing a second text sequence in the first language; and outputting, by the digital model, the second text sequence in the first language. The modifying may include any one or more of: deleting text from the first text sequence, 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. The digital model may have been fine-tuned, after having been machine-learned, using a subset of values of model parameters associated with an encoding layer or an embedding layer or both the encoding layer and the embedding layer.

Proficiency and native language-adapted grammatical error correction

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Brooklyn, NY, United States of America

Maria Nadejde