Selection of domain-adapted translation subcorpora
First Claim
1. A computer-implemented selection system, comprising:
- linguistic data corpora that include an in-domain corpus and an out-domain corpus for domain adaptation for machine translation model training, the in-domain corpus and the out-domain corpus including multi-lingual data translated to the corpora in parallel;
a relevance component that selects relevant multi-lingual data from the out-domain corpus based on a similarity measure, the similarity measure considering a difference of cross-entropy scores according to an in-domain language model and an out-domain language model, the relevant multi-lingual data utilized in combination with the in-domain corpus or in isolation without the in-domain corpus; and
a processor that executes computer-executable instructions associated with at least the relevance component.
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Abstract
An architecture is discussed that provides the capability to subselect the most relevant data from an out-domain corpus to use either in isolation or in combination conjunction with in-domain data. The architecture is a domain adaptation for machine translation that selects the most relevant sentences from a larger general-domain corpus of parallel translated sentences. The methods for selecting the data include monolingual cross-entropy measure, monolingual cross-entropy difference, bilingual cross entropy, and bilingual cross-entropy difference. A translation model is trained on both the in-domain data and an out-domain subset, and the models can be interpolated together to boost performance on in-domain translation tasks.
33 Citations
20 Claims
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1. A computer-implemented selection system, comprising:
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linguistic data corpora that include an in-domain corpus and an out-domain corpus for domain adaptation for machine translation model training, the in-domain corpus and the out-domain corpus including multi-lingual data translated to the corpora in parallel; a relevance component that selects relevant multi-lingual data from the out-domain corpus based on a similarity measure, the similarity measure considering a difference of cross-entropy scores according to an in-domain language model and an out-domain language model, the relevant multi-lingual data utilized in combination with the in-domain corpus or in isolation without the in-domain corpus; and a processor that executes computer-executable instructions associated with at least the relevance component. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A computer-implemented selection method, comprising acts of:
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receiving a set of trained in-domain language models, one for each language of multi-lingual sentences based on an in-domain corpus and a set of trained out-domain language models, one for each language of multi-lingual sentences based on an out-domain corpus; computing similarity scores for each of the sentences of the out-domain corpus, the scores obtained using a similarity measure as applied to the sentences against the in-domain language model and the out-domain language model; ranking the sentences based on the scores; selecting a set of sentences from the out-domain corpus based on the ranked scores; building a translation model based on either the set selected from the out-domain corpus, or a combination of the set selected from the out-domain corpus and the in-domain corpus; and utilizing a processor that executes instructions stored in memory to perform at least one of the acts of computing, ranking, selecting, or building. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer-implemented selection method, comprising acts of:
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receiving an in-domain corpus of bilingual sentences and an out-domain corpus of bilingual sentences; generating an in-domain machine translation system from the in-domain corpus; training an in-domain language model based on the in-domain corpus and training an out-domain language model based on the out-domain corpus; applying a similarity measure to a sentence of the out-domain corpus and the in-domain language model, and to the sentence and the out-domain language model, to obtain similarity scores; selecting relevant sentences from the out-domain corpus based on the scores to create a subselected out-domain translation system; combining the in-domain machine translation system and the subselected out-domain translation system to create a domain adapted machine translation system; and utilizing a processor that executes instructions stored in memory to perform at least one of the acts of generating, training, applying, selecting, or combining. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification