1. A translation method adapted to a domain of interest comprising: receiving a source text string comprising a sequence of source words in a source language; generating a set of candidate translations of the source text string, each candidate translation comprising a sequence of target words in a target language; and with a processor, identifying an optimal translation from the set of candidate translations as a function of at least one domain-adapted feature, the at least one domain-adapted feature being computed based on: bilingual probabilities, each bilingual probability being for a source text fragment and a target text fragment of the source text string and candidate translation respectively, the bilingual probabilities being estimated on an out-of-domain parallel corpus comprising source and target strings; and monolingual probabilities for text fragments of one of the source text string and candidate translation, the monolingual probabilities being estimated on an in-domain monolingual corpus, wherein the domain-adapted feature comprises at least one of: a) a forward domain-adapted lexical feature which is a function of ∑ ( i , j ) ∈ a ⁢ ⁢ w in ⁡ ( s i ❘ t j ) where w in (s i |t j ) is an adapted word probability and is a function of a product of w out (t j |s i ) and w in (s i ); b) a reverse domain-adapted lexical feature which is a function of ∑ ( i , j ) ∈ a ⁢ ⁢ w in ⁡ ( t j ❘ s i ) where w in (t j |s i ) is an adapted word probability and is a function of a product of w out (t j |s i ) and w in (t j ); c) a forward domain-adapted phrasal feature which is a function of: ∑ ( i , j ) ∈ a ⁢ phr in ⁡ ( s _ i ❘ t _ j ) , where phr in ( s i | t j ) is an adapted phrase probability and is a function of a product of phr out ( t j | s i ) and p in ( s i ); d) a reverse domain-adapted phrasal feature which is a function of: ∑ ( i , j ) ∈ a ⁢ phr in ⁡ ( t _ j ❘ s _ i ) , where phr in ( t j | s i ) is an adapted phrase probability and is a function of a product of phr out ( s i | t j ) and p in ( t j ); where s i and t j represent words of the source string and candidate translation respectively which are aligned in an alignment α of the source string and candidate translation, w out (t j |s i ) represents the bilingual probability, which is a word probability for target word t j in the presence of source word s i , derived from the parallel corpus, and w in (s i ) represents the monolingual probability, which is the word probability for source word s i derived from the in-domain monolingual corpus; s i and t j represent phrases of the source string and candidate translation respectively which are aligned in the alignment α of the source string and candidate translation, phr out ( t j | s i )represents the bilingual probability, which is a phrasal probability for target phrase t j in the presence of source phrase s i , derived from the parallel corpus, p in ( s i ) represents the monolingual probability, which is the phrasal probability for source phrase s i derived from the in-domain monolingual corpus, and p in ( t j ) represents the monolingual probability, which is the phrasal probability for target phrase t j derived from the in-domain monolingual corpus.
1. A translation method adapted to a domain of interest comprising: receiving a source text string comprising a sequence of source words in a source language; generating a set of candidate translations of the source text string, each candidate translation comprising a sequence of target words in a target language; and with a processor, identifying an optimal translation from the set of candidate translations as a function of at least one domain-adapted feature, the at least one domain-adapted feature being computed based on: bilingual probabilities, each bilingual probability being for a source text fragment and a target text fragment of the source text string and candidate translation respectively, the bilingual probabilities being estimated on an out-of-domain parallel corpus comprising source and target strings; and monolingual probabilities for text fragments of one of the source text string and candidate translation, the monolingual probabilities being estimated on an in-domain monolingual corpus, wherein the domain-adapted feature comprises at least one of: a) a forward domain-adapted lexical feature which is a function of ∑ ( i , j ) ∈ a ⁢ ⁢ w in ⁡ ( s i ❘ t j ) where w in (s i |t j ) is an adapted word probability and is a function of a product of w out (t j |s i ) and w in (s i ); b) a reverse domain-adapted lexical feature which is a function of ∑ ( i , j ) ∈ a ⁢ ⁢ w in ⁡ ( t j ❘ s i ) where w in (t j |s i ) is an adapted word probability and is a function of a product of w out (t j |s i ) and w in (t j ); c) a forward domain-adapted phrasal feature which is a function of: ∑ ( i , j ) ∈ a ⁢ phr in ⁡ ( s _ i ❘ t _ j ) , where phr in ( s i | t j ) is an adapted phrase probability and is a function of a product of phr out ( t j | s i ) and p in ( s i ); d) a reverse domain-adapted phrasal feature which is a function of: ∑ ( i , j ) ∈ a ⁢ phr in ⁡ ( t _ j ❘ s _ i ) , where phr in ( t j | s i ) is an adapted phrase probability and is a function of a product of phr out ( s i | t j ) and p in ( t j ); where s i and t j represent words of the source string and candidate translation respectively which are aligned in an alignment α of the source string and candidate translation, w out (t j |s i ) represents the bilingual probability, which is a word probability for target word t j in the presence of source word s i , derived from the parallel corpus, and w in (s i ) represents the monolingual probability, which is the word probability for source word s i derived from the in-domain monolingual corpus; s i and t j represent phrases of the source string and candidate translation respectively which are aligned in the alignment α of the source string and candidate translation, phr out ( t j | s i )represents the bilingual probability, which is a phrasal probability for target phrase t j in the presence of source phrase s i , derived from the parallel corpus, p in ( s i ) represents the monolingual probability, which is the phrasal probability for source phrase s i derived from the in-domain monolingual corpus, and p in ( t j ) represents the monolingual probability, which is the phrasal probability for target phrase t j derived from the in-domain monolingual corpus. 13. The method of claim 1 , wherein the scoring function has the general form: score = ∑ k ⁢ ⁢ λ k ⁢ Φ k ⁡ ( s , t ) ( 1 ) where k is at least two; Φ k represent the features, at least one of which is the in-domain feature; and λ k represent the weights, at least one of which is the weight for the in-domain feature.