Abstract: A system and a method for phrase-based translation are disclosed. The method includes receiving source language text to be translated into target language text. One or more dynamic bi-phrases are generated, based on the source text and the application of one or more rules, which may be based on user descriptions. A dynamic feature value is associated with each of the dynamic bi-phrases. For a sentence of the source text, static bi-phrases are retrieved from a bi-phrase table, each of the static bi-phrases being associated with one or more values of static features. Any of the dynamic bi-phrases which each cover at least one word of the source text are also retrieved, which together form a set of active bi-phrases. Translation hypotheses are generated using active bi-phrases from the set and scored with a translation scoring model which takes into account the static and dynamic feature values of the bi-phrases used in the respective hypothesis. A translation, based on the hypothesis scores, is then output.

Abstract: Rejection sampling is performed to acquire at least one target language translation for a source language string s in accordance with a phrase-based statistical translation model p(x)=p(t, a|s) where t is a candidate translation, a is a candidate alignment comprising a biphrase sequence generating the candidate translation t, and x is a sequence representing the candidate alignment a. The rejection sampling uses a proposal distribution comprising a weighted finite state automaton (WFSA) q(n) that is refined responsive to rejection of a sample x* obtained in a current iteration of the rejection sampling to generate a refined WFSA q(n+1) for use in a next iteration of the rejection sampling. The refined WFSA q(n+1) is selected to satisfy the criteria p(x)?q(n+1)(x)?q(n)(x) for all x?X and q(n+1)(x*)<q(n)(x*) where the space X is the set of sequences x corresponding to candidate alignments a that generate candidate translations t for the source language string s.

Abstract: Rejection sampling is performed to acquire at least one target language translation for a source language string s in accordance with a phrase-based statistical translation model p(x)=p(t, a|s) where t is a candidate translation, a is a candidate alignment comprising a biphrase sequence generating the candidate translation t, and x is a sequence representing the candidate alignment a. The rejection sampling uses a proposal distribution comprising a weighted finite state automaton (WFSA) q(n) that is refined responsive to rejection of a sample x* obtained in a current iteration of the rejection sampling to generate a refined WFSA q(n+1) for use in a next iteration of the rejection sampling. The refined WFSA q(n+1) is selected to satisfy the criteria p(x)?q(n+1)(x)?q(n)(x) for all x?X and q(n+1)(x*)<q(n)(x*) where the space X is the set of sequences x corresponding to candidate alignments a that generate candidate translations t for the source language string s.

Abstract: A system and a method for phrase-based translation are disclosed. The method includes receiving source language text to be translated into target language text. One or more dynamic bi-phrases are generated, based on the source text and the application of one or more rules, which may be based on user descriptions. A dynamic feature value is associated with each of the dynamic bi-phrases. For a sentence of the source text, static bi-phrases are retrieved from a bi-phrase table, each of the static bi-phrases being associated with one or more values of static features. Any of the dynamic bi-phrases which each cover at least one word of the source text are also retrieved, which together form a set of active bi-phrases. Translation hypotheses are generated using active bi-phrases from the set and scored with a translation scoring model which takes into account the static and dynamic feature values of the bi-phrases used in the respective hypothesis. A translation, based on the hypothesis scores, is then output.