Optimizing Statistical Machine Translation for Text Simplification

Optimizing Statistical Machine Translation for Text Simplification Wei Xu1, Courtney Napoles2, Ellie Pavlick1, Quanze Chen1 and Chris Callison-Burch1 1 Computer and Information Science Department University of Pennsylvania {xwe, epavlick, cquanze, ccb}@seas.upenn.edu 2 Department of Computer Science Johns Hopkins University courtneyn@jhu.edu Abstract Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a man- ually simplified parallel corpus. These meth- ods are limited by the quality and quantity of manually simplified corpora, which are expen- sive to build. In this paper, we conduct an in- depth adaptation of statistical machine trans- lation to perform text simplification, taking advantage of large-scale paraphrases learned from bilingual texts and a small amount of manual simplifications with multiple refer- ences. Our work is the first to design auto- matic metrics that are effective for tuning and evaluating simplification systems, which will facilitate iterative development for this task. 1 Introduction The goal of text simplification is to rewrite an input text so that the output is more readable. Text sim- plification has applications for reducing input com- plexity for natural language processing (Siddharthan et al., 2004; Miwa et al., 2010; Chen et al., 2012b) and providing reading aids for people with lim- ited language skills (Petersen and Ostendorf, 2007; Watanabe et al., 2009; Allen, 2009; De Belder and Moens, 2010; Siddharthan and Katsos, 2010) or lan- guage impairments such as dyslexia (Rello et al., 2013), autism (Evans et al., 2014), and aphasia (Car- roll et al., 1999). It is widely accepted that sentence simplification can be implemented by three major types of oper- ations: splitting, deletion and paraphrasing (Feng, 2008). The splitting operation decomposes a long sentence into a sequence of shorter sentences. Dele- tion removes less important parts of a sentence. The paraphrasing operation includes reordering, lexical substitutions and syntactic transformations. While sentence splitting (Siddharthan, 2006; Petersen and Ostendorf, 2007; Narayan and Gardent, 2014; An- grosh et al., 2014) and deletion (Knight and Marcu 2002; Clarke and Lapata 2006; Filippova and Strube 2008; Filippova et al. 2015; Rush et al. 2015; and others) have been intensively studied, there has been considerably less research on developing new para- phrasing models for text simplification — most pre- vious work has used off-the-shelf statistical machine translation (SMT) technology and achieved reason- able results (Coster and Kauchak, 2011a,b; Wubben et al., 2012; ˇStajner et al., 2015). However, they have either treated the judgment technology as a black (Coster and Kauchak, 2011a,b; Narayan and Gar- dent, 2014; Angrosh et al., 2014; ˇStajner et al., 2015) or they have been limited to modifying only one as- pect of it, such as the translation model (Zhu et al., 2010; Woodsend and Lapata, 2011) or the reranking component (Wubben et al., 2012). In this paper, we present a complete adaptation of a syntax-based machine translation framework to perform simplification. Our methodology poses text simplification as a paraphrasing problem: given an input text, rewrite it subject to the constraints that the output should be simpler than the …

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