Amazon Technologies, Inc.

Alon Lavie

Gregory Alan Hanneman

Michael Denkowski

Gavin Richard Jewell

William Joseph Kaper

Alexandre Alexandrovich Klementiev

Matthew Ryan Fiorillo

Robert Thomas Olszewski

Laura Josephine Kieras

Gyorgy Istvan Szarvas

Ann Clifton

Austin Matthews

Christopher James Dyer

Kartik Goyal

Ehud Hershkovich

Technologies are disclosed for utilizing artificial intelligence-based machine translation to augment document summarization. Text can be extracted from a document in a first language. Machine translation can be utilized to translate the text from the first language to a second language. The translated text can be used to identify documents in the second language that include support for the translated text. A user interface can be provided that indicates the number of documents in the second language that provide support for the extracted text. Documents in the first language can also be translated to the second language. Documents that provide support for a text string can be identified in the documents translated to the second language and in other documents in the second language. A user interface can be provided that indicates the number of documents in the first language and the second language that provide support for the text.

Utilizing artificial intelligence-based machine translation to augment document summarization

A machine translation system capable of clustering training data and performing dynamic domain adaptation is disclosed. An unsupervised domain clustering process is utilized to identify domains in general training data that can include in-domain training data and out-of-domain training data. Segments in the general training data are then assigned to the domains in order to create domain-specific training data. The domain-specific training data is then utilized to create domain-specific language models, domain-specific translation models, and domain-specific model weights for the domains. An input segment to be translated can be assigned to a domain at translation time. The domain-specific model weights for the assigned domain can be utilized to translate the input segment.

Multi-domain machine translation system with training data clustering and dynamic domain adaptation

Technologies are disclosed herein for statistical machine translation. In particular, the disclosed technologies include extensions to conventional machine translation pipelines: the use of multiple domain-specific and non-domain-specific dynamic language translation models and language models; cluster-based language models; and large-scale discriminative training. Incremental update technologies are also disclosed for use in updating a machine translation system in four areas: word alignment; translation modeling; language modeling; and parameter estimation. A mechanism is also disclosed for training and utilizing a runtime machine translation quality classifier for estimating the quality of machine translations without the benefit of reference translations. The runtime machine translation quality classifier is generated in a manner to offset imbalances in the number of training instances in various classes, and to assign a greater penalty to the misclassification of lower-quality translations as higher-quality translations than to misclassification of higher-quality translations as lower-quality translations.

Optimized statistical machine translation system with rapid adaptation capability

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Pittsburgh, PA, United States of America

Alon Lavie