We propose a novel approach to improve SMT via paraphrase rules which are automatically extracted from the bilingual training data . Without using extra paraphrase resources , we acquire the rules by comparing the source side of the parallel corpus with the target-to-source translations of the target side . Besides the word and phrase paraphrases , the acquired paraphrase rules mainly cover the structured paraphrases on the sentence level . These rules are employed to enrich the SMT inputs for translation quality improvement . The experimental results show that our proposed approach achieves significant improvements of 1.6~3.6 points of BLEU in the oral domain and 0.5~1 points in the news domain . This work was done when the first author was visiting Baidu . The translation quality of the SMT system is highly related to the coverage of translation models . However , no matter how much data is used for training , it is still impossible to completely cover the unlimited input sentences . This problem is more serious for online SMT systems in real-world applications . Naturally , a solution to the coverage problem is to bridge the gaps between the input sentences and the translation models , either from the input side , which targets on rewriting the input sentences to the MT-favored expressions , or from the side of translation models , which tries to enrich the translation models to cover more expressions . In recent years , paraphrasing has been proven useful for improving SMT quality . The proposed methods can be classified into two categories according to the paraphrase targets : ( 1 ) enrich translation models to cover more bilingual expressions ; ( 2 ) paraphrase the input sentences to reduce OOVs or generate multiple inputs . In the first category , He et al . ( 2011 ) , Bond et al . ( 2008 ) and Nakov ( 2008 ) enriched the SMT models via paraphrasing the training corpora . Kuhn et al . ( 2010 ) and Max ( 2010 ) used paraphrases to smooth translation models . For the second category , previous studies mainly focus on finding translations for unknown terms using phrasal paraphrases . Callison-Burch et al . ( 2006 ) and Marton et al . ( 2009 ) paraphrase unknown terms in the input sentences using phrasal paraphrases extracted from bilingual and monolingual corpora . Mirkin et al . ( 2009 ) rewrite OOVs with entailments and paraphrases acquired from WordNet . Onishi et al . ( 2010 ) and Du et al . ( 2010 ) use phrasal paraphrases to build a word lattice to get multiple input candidates . In the above methods , only word or phrasal paraphrases are used for input sentence rewriting . No structured paraphrases on the sentence level have been investigated . However , the information in the sentence level is very important for disambiguation . For example , we can only substitute play with drama in a context related to stage or theatre . Phrasal paraphrase substitutions can hardly solve such kind of problems . In this paper , we propose a method that rewrites the input sentences of the SMT system using automatically extracted paraphrase rules which can capture structures on sentence level in addition to paraphrases on the word or phrase level . Without extra paraphrase resources , a novel approach is proposed to acquire paraphrase rules from the bilingual training corpus based on the results of Forward-Translation and Back-Translation . The rules target on rewriting the input sentences to an MT-favored expression to ensure a better translation . The paraphrase rules cover all kinds of paraphrases on the word , phrase and sentence levels , enabling structure reordering , word or phrase insertion , deletion and substitution . The experimental results show that our proposed approach achieves significant improvements of 1.6~3.6 points of BLEU in the oral domain and 0.5~1 points in the news domain . The remainder of the paper is organized as follows : Section 2 makes a comparison between the Forward-Translation and Back-Translation . Section 3 introduces our methods that extract paraphrase rules from the bilingual corpus of SMT . Section 4 describes the strategies for constructing word lattice with paraphrase rules . The experimental results and some discussions are presented in Section 5 and Section 6 . Section 7 compares our work to the previous researches . Finally , Section 8 concludes the paper and suggests directions for future work . In this paper , we propose a novel method for extracting paraphrase rules by comparing the source side of bilingual corpus to the target-tosource translation of the target side . The acquired paraphrase rules are employed to enrich the SMT inputs , which target on rewriting the input sentences to an MT-favored form . The paraphrase rules cover all kinds of paraphrases on the word , phrase and sentence levels , enabling structure reordering , word or phrase insertion , deletion and substitution . Experimental results show that the paraphrase rules can improve SMT quality in both the oral and news domains . The manual investigation on oral translation results indicate that the paraphrase rules capture four kinds of MTfavored transformation to ensure translation quality improvement .
Incorporating paraphrases improves statistical machine translation however no works investigate sentence level paraphrases.
They propose to use bilingual training data to obtain paraphrase rules on word, phrase and sentence levels to rewrite inputs to be MT-favored.
The acquired paraphrase rules improve translation qualities in oral and news domains.