This paper proposes a novel method that exploits multiple resources to improve statistical machine translation ( SMT ) based paraphrasing . In detail , a phrasal paraphrase table and a feature function are derived from each resource , which are then combined in a log-linear SMT model for sentence-level paraphrase generation . Experimental results show that the SMT-based paraphrasing model can be enhanced using multiple resources . The phrase-level and sentence-level precision of the generated paraphrases are above 60 % and 55 % , respectively . In addition , the contribution of each resource is evaluated , which indicates that all the exploited resources are useful for generating paraphrases of high quality . Paraphrases are alternative ways of conveying the same meaning . Paraphrases are important in many natural language processing ( NLP ) applications , such as machine translation ( MT ) , question answering ( QA ) , information extraction ( IE ) , multidocument summarization ( MDS ) , and natural language generation ( NLG ) . This paper addresses the problem of sentencelevel paraphrase generation , which aims at generating paraphrases for input sentences . An example of sentence-level paraphrases can be seen below : S1 : The table was set up in the carriage shed . S2 : The table was laid under the cart-shed . Paraphrase generation can be viewed as monolingual machine translation ( Quirk et al . , 2004 ) , which typically includes a translation model and a language model . The translation model can be trained using monolingual parallel corpora . However , acquiring such corpora is not easy . Hence , data sparseness is a key problem for the SMT-based paraphrasing . On the other hand , various methods have been presented to extract phrasal paraphrases from different resources , which include thesauri , monolingual corpora , bilingual corpora , and the web . However , little work has been focused on using the extracted phrasal paraphrases in sentence-level paraphrase generation . In this paper , we exploit multiple resources to improve the SMT-based paraphrase generation . In detail , six kinds of resources are utilized , including : ( 1 ) an automatically constructed thesaurus , ( 2 ) a monolingual parallel corpus from novels , ( 3 ) a monolingual comparable corpus from news articles , ( 4 ) a bilingual phrase table , ( 5 ) word definitions from Encarta dictionary , and ( 6 ) a corpus of similar user queries . Among the resources , ( 1 ) , ( 2 ) , ( 3 ) , and ( 4 ) have been investigated by other researchers , while ( 5 ) and ( 6 ) are first used in this paper . From those resources , six phrasal paraphrase tables are extracted , which are then used in a log-linear SMTbased paraphrasing model . Both phrase-level and sentence-level evaluations were carried out in the experiments . In the former one , phrase substitutes occurring in the paraphrase sentences were evaluated . While in the latter one , the acceptability of the paraphrase sentences was evaluated . Experimental results show that : ( 1 ) The SMT-based paraphrasing is enhanced using multiple resources . The phrase-level and sentence-level precision of the generated paraphrases exceed 60 % and 55 % , respectively . ( 2 ) Although the contributions of the resources differ a lot , all the resources are useful . ( 3 ) The performance of the method varies greatly on different test sets and it performs best on the test set of news sentences , which are from the same source as most of the training data . The rest of the paper is organized as follows : Section 2 reviews related work . Section 3 introduces the log-linear model for paraphrase generation . Section 4 describes the phrasal paraphrase extraction from different resources . Section 5 presents the parameter estimation method . Section 6 shows the experiments and results . Section 7 draws the conclusion . This paper proposes a method that improves the SMT-based sentence-level paraphrase generation using phrasal paraphrases automatically extracted from different resources . Our contribution is that we combine multiple resources in the framework of SMT for paraphrase generation , in which the dic-tionary definitions and similar user queries are first used as phrasal paraphrases . In addition , we analyze and compare the contributions of different resources . Experimental results indicate that although the contributions of the exploited resources differ a lot , they are all useful to sentence-level paraphrase generation . Especially , the dictionary definitions and similar user queries are effective for paraphrasing some certain types of phrases . In the future work , we will try to use syntactic and context constraints in paraphrase generation to enhance the acceptability of the paraphrases . In addition , we will extract paraphrase patterns that contain more structural variation and try to combine the SMT-based and pattern-based systems for sentencelevel paraphrase generation .

Challenge: Paraphrase generation requires monolingual parallel corpora which is not easily obtainable, and few works focus on using the extracted phrasal paraphrases in sentence-level paraphrase generation.
Approach: They propose to exploit six paraphrase resources to extract phrasal paraphrase tables that are further used to build a log-linear statistical machine translation-based paraphrasing model.
Outcome: They show that using multiple resources enhances paraphrase generation quality in precision on phrase and sentence level especially when they are similar to user queries.