Optimized statistical machine translation system with rapid adaptation capability
First Claim
1. 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.
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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.
75 Citations
21 Claims
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1. An apparatus, comprising:
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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. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer-implemented method for classifying a quality of translated segments generated by a machine translation system, the method comprising:
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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. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. 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:
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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. - View Dependent Claims (18, 19, 20, 21)
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Specification