Combining Multiple Resources to Improve SMT-based Paraphrasing Model

Proceedings of ACL-08: HLT, pages 1021–1029, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Combining Multiple Resources to Improve SMT-based Paraphrasing Model∗ Shiqi Zhao1, Cheng Niu2, Ming Zhou2, Ting Liu1, Sheng Li1 1Harbin Institute of Technology, Harbin, China {zhaosq,tliu,lisheng}@ir.hit.edu.cn 2Microsoft Research Asia, Beijing, China {chengniu,mingzhou}@microsoft.com Abstract This paper proposes a novel method that ex- ploits multiple resources to improve statisti- cal machine translation (SMT) based para- phrasing. In detail, a phrasal paraphrase ta- ble and a feature function are derived from each resource, which are then combined in a log-linear SMT model for sentence-level para- phrase 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 contribu- tion of each resource is evaluated, which indi- cates that all the exploited resources are useful for generating paraphrases of high quality. 1 Introduction 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 an- swering (QA), information extraction (IE), multi- document summarization (MDS), and natural lan- guage generation (NLG). This paper addresses the problem of sentence- level paraphrase generation, which aims at generat- ing 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. ∗This research was finished while the first author worked as an intern in Microsoft Research Asia. Paraphrase generation can be viewed as monolin- gual machine translation (Quirk et al., 2004), which typically includes a translation model and a lan- guage model. The translation model can be trained using monolingual parallel corpora. However, ac- quiring such corpora is not easy. Hence, data sparse- ness is a key problem for the SMT-based paraphras- ing. On the other hand, various methods have been presented to extract phrasal paraphrases from dif- ferent resources, which include thesauri, monolin- gual corpora, bilingual corpora, and the web. How- ever, little work has been focused on using the ex- tracted phrasal paraphrases in sentence-level para- phrase generation. In this paper, we exploit multiple resources to improve the SMT-based paraphrase generation. In detail, six kinds of resources are utilized, includ- ing: (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 simi- lar 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 ex- tracted, which are then used in a log-linear SMT- based 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 …

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