US 9,959,271 B1Granted

Optimized statistical machine translation system with rapid adaptation capability

AssigneeAmazon Technologies, Inc.

InventorsKartik Goyal, Alon Lavie, Michael Denkowski, Gregory Alan Hanneman, Matthew Ryan Fiorillo, Robert Thomas Olszewski, Ehud Hershkovich, William Joseph Kaper, Alexandre Alexandrovich Klementiev, Gavin R. Jewell

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Abstract

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.

Claims21

Dependent claims

An apparatus, comprising:

one or more processors;
and
one or more non-transitory computer-readable storage media having instructions stored thereupon which are executable by the one or more processors and which, when executed, cause the apparatus to:
determine a number of out-of-vocabulary words in input text segments;
generate an estimated difficulty feature score of a supervised machine learning model in translating the input text segments based, at least in part, on the number of out-of-vocabulary words;
modify a misclassification cost associated with the supervised machine learning model, stored in a memory, to offset an imbalance between a plurality of classes of training data utilized to train a machine translation quality classifier to classify a quality of machine translated text segments, the training data comprising one or more feature scores including the estimated difficulty feature score;
modify a loss function associated with the supervised machine learning model stored in the memory to penalize a misclassification of a lower-quality text segment as a higher-quality text segment more greatly than a misclassification of a higher-quality text segment as a lower-quality text segment;
train the machine translation quality classifier utilizing the supervised machine learning model based, at least in part on the misclassification cost and the loss function;
cause the machine translation quality classifier to be deployed to a computer in a service provider network;
and
utilize the machine translation quality classifier is utilized to classify a quality of translated segments received from a machine translation system operating in the service provider network into one of the plurality of classes in real time.

8 more elements

A computer-implemented method for classifying a quality of translated segments generated by a machine translation system, the method comprising:

generating an estimated difficulty feature score of a supervised machine learning model in translating input text segments based, at least in part, on a number of out-of-vocabulary words in the input text segments;
training a machine translation quality classifier stored in a memory to classify the quality of the translated segments utilizing the supervised machine learning model configured with
a misclassification cost configured to offset an imbalance between a plurality of classes of training data, the training data comprising one or more feature scores associated with machine translated segments of a target language and correct class labels for the machine translated segments in the target language, the one or more feature scores including the estimated difficulty feature score;
and
a loss function configured to penalize a misclassification of a lower-quality translated segment as a higher-quality translated segment more greatly than a misclassification of a higher-quality translated segment as a lower-quality translated segment;
and
utilizing the machine translation quality classifier at a computer in a service provider network to classify the quality of the translated segments generated by the machine translation system into the plurality of classes.

4 more elements

A non-transitory computer-readable storage media having instructions stored thereupon which are executable by one or more processors and which, when executed, cause the one or more processors to:

generate an estimated difficulty feature score of a supervised machine learning model in translating input text segments based, at least in part, on a number of out-of-vocabulary words in the input text segments;
train a machine translation quality classifier stored in a memory to classify a quality of the translated segments utilizing the supervised machine learning model configured with
a misclassification cost configured to offset an imbalance between a plurality of classes of training data, the training data comprising feature scores associated with machine translated segments in a target language, the feature scores including the estimated difficulty feature score;
and
a loss function configured to penalize a misclassification of a lower-quality translated segment as a higher-quality translated segment more greatly than a misclassification of a higher-quality translated segment as a lower-quality translated segment;
and
utilize the machine translation quality classifier at a computer operating in a service provider network to classify the quality of the translated segments generated by the machine translation system into the plurality of classes.

4 more elements

Dependencies are inferred from the claim text.