TRAINING MARKOV RANDOM FIELD-BASED TRANSLATION MODELS USING GRADIENT ASCENT
First Claim
1. A system that translates an input string in a source language to an output string in a target language, comprising:
- a statistical machine translation (SMT) system that receives the input string in the source language and generates the output string in the target language based upon the input string in the source language, wherein the SMT system comprises;
a Markov random field (MRF)-based phrase translation model; and
a decoder component that evaluates scores of phrase translation pair hypotheses between the source language and the target language utilizing the MRF-based phrase translation model based upon a source phrase included in the input string in the source language, wherein the decoder component generates the output string in the target language as a function of the scores of the phrase translation pair hypotheses.
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Abstract
Various technologies described herein pertain to training and utilizing a general, statistical framework for modeling translation via Markov random fields (MRFs). An MRF-based translation model can be employed in a statistical machine translation (SMT) system. The MRF-based translation model allows for arbitrary features extracted from a phrase pair to be incorporated as evidence. The parameters of the model are estimated using a large-scale discriminative training approach based on stochastic gradient ascent and an N-best list based expected Bilingual Evaluation Understudy (BLEU) as an objective function.
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Citations
20 Claims
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1. A system that translates an input string in a source language to an output string in a target language, comprising:
a statistical machine translation (SMT) system that receives the input string in the source language and generates the output string in the target language based upon the input string in the source language, wherein the SMT system comprises; a Markov random field (MRF)-based phrase translation model; and a decoder component that evaluates scores of phrase translation pair hypotheses between the source language and the target language utilizing the MRF-based phrase translation model based upon a source phrase included in the input string in the source language, wherein the decoder component generates the output string in the target language as a function of the scores of the phrase translation pair hypotheses. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A method of training a Markov random field (MRF)-based phrase translation model for a statistical machine translation (SMT) system, comprising:
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generating respective N-best lists of translation hypotheses for source sentences in training data; computing respective objective function scores for the translation hypotheses; computing respective translation scores for the translation hypotheses using current parameters of the MRF-based phrase translation model for the SMT system; and updating the parameters of the MRF-based phrase translation model utilizing stochastic gradient ascent based on the objective function scores and the translation scores for the translation hypotheses. - View Dependent Claims (14, 15, 16, 17, 18)
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19. A method of translating an input string in a source language to an output string in a target language, comprising:
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extracting features of phrase translation pair hypotheses for a source phrase included in the input string in the source language, wherein the phrase translation pair hypotheses for the source phrase comprise the source phrase included in the input string in the source language and candidate target phrases in the target language, and wherein the features of the phrase translation pair hypotheses for the source phrase comprise phrase-pair features, word-pair features, and triplet features; evaluating scores of phrase translation pair hypotheses between the source language and the target language based upon the features of the phrase translation pair hypotheses for the source phrase included in the input string in the source language; and generating the output string in the target language as a function of the scores of the phrase translation pair hypotheses. - View Dependent Claims (20)
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Specification