Multi-domain machine translation system with training data clustering and dynamic domain adaptation
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;
identify a plurality of domains in general training data comprising client training data and background training data, the client training data and the background training data being expressed in a source language and a target language;
assign segments in the general training data to one of the plurality of domains to create domain-specific training data for the plurality of domains;
generate a domain-specific translation model for the plurality of domains using the domain-specific training data;
generate a domain-specific language model for the plurality of domains using the domain-specific training data;
extract domain-specific tuning data from the domain-specific training data;
generate candidate translations using the domain-specific tuning data, the domain-specific language models, and the domain-specific translation models;
determine feature scores corresponding to individual ones of the domain-specific language models and the domain-specific translation models based at least in part on the candidate translations;
learn, based at least in part on the domain-specific tuning data and the feature scores, domain-specific model weights associated with the feature scores;
generate a translation package comprising the domain-specific language models, the domain-specific translation models, and the domain-specific model weights;
receive a request to translate an input segment in the source language into the target language;
identify individual domain-specific model weights to be utilized to translate the input segment;
identify one or more phrases associated with the input segment; and
translate the input segment into the target language by selecting, based at least in part on the individual domain-specific model weights, one or more candidate translations of the candidate translations corresponding to the one or more phrases of the input segment.
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Abstract
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.
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Citations
20 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; identify a plurality of domains in general training data comprising client training data and background training data, the client training data and the background training data being expressed in a source language and a target language; assign segments in the general training data to one of the plurality of domains to create domain-specific training data for the plurality of domains; generate a domain-specific translation model for the plurality of domains using the domain-specific training data; generate a domain-specific language model for the plurality of domains using the domain-specific training data; extract domain-specific tuning data from the domain-specific training data; generate candidate translations using the domain-specific tuning data, the domain-specific language models, and the domain-specific translation models; determine feature scores corresponding to individual ones of the domain-specific language models and the domain-specific translation models based at least in part on the candidate translations; learn, based at least in part on the domain-specific tuning data and the feature scores, domain-specific model weights associated with the feature scores; generate a translation package comprising the domain-specific language models, the domain-specific translation models, and the domain-specific model weights; receive a request to translate an input segment in the source language into the target language; identify individual domain-specific model weights to be utilized to translate the input segment; identify one or more phrases associated with the input segment; and translate the input segment into the target language by selecting, based at least in part on the individual domain-specific model weights, one or more candidate translations of the candidate translations corresponding to the one or more phrases of the input segment. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-implemented method for statistical machine translation, the method comprising:
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assigning segments in general training data to one of a plurality of domains to create domain-specific training data for the plurality of domains; generating domain-specific translation models for the plurality of domains using the domain-specific training data; generating domain-specific language models for the plurality of domains using the domain-specific training data; extracting domain-specific tuning data from the domain-specific training data; generating candidate translations using the domain-specific tuning data, the domain-specific language models, and the domain-specific translation models; determining feature scores corresponding to individual ones of the domain-specific language models and the domain-specific translation models based at least in part on the candidate translations; learning, based at least in part on the domain-specific tuning data and the feature scores, domain-specific model weights for the plurality of domains; utilizing the domain-specific language models, the domain-specific translation models, and the domain-specific model weights to translate input segments from a source language to a target language; receiving a request to translate a particular input segment in the source language into the target language; identifying individual domain-specific model weights to be utilized to translate the particular input segment; identifying one or more phrases associated with the particular input segment; and translating the particular input segment into the target language by selecting, based at least in part on the individual domain-specific model weights, one or more candidate translations of the candidate translations corresponding to the one or more phrases of the particular input segment. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-readable storage media having instructions stored thereupon that are executable by one or more processors and which, when executed, cause the one or more processors to:
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receive a request to translate an input segment in a source language into a target language; responsive to receiving the request, identify a domain for the input segment; identify individual domain-specific model weights for the identified domain, wherein the domain-specific model weights are learned based at least in part on domain-specific tuning data and feature scores corresponding to individual ones of domain-specific language models and domain-specific translation models; identify one or more phrases associated with the input segment; and translate the input segment from the source language into the target language by selecting, based at least in part on the individual domain-specific model weights, one or more candidate translations corresponding to the one or more phrases of the input segment. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification