A Generate and Rank Approach to Sentence Paraphrasing

Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 96–106, Edinburgh, Scotland, UK, July 27–31, 2011. c⃝2011 Association for Computational Linguistics A Generate and Rank Approach to Sentence Paraphrasing Prodromos Malakasiotis∗ and Ion Androutsopoulos∗+ ∗Department of Informatics, Athens University of Economics and Business, Greece +Digital Curation Unit – IMIS, Research Centre “Athena”, Greece Abstract We present a method that paraphrases a given sentence by first generating candidate para- phrases and then ranking (or classifying) them. The candidates are generated by ap- plying existing paraphrasing rules extracted from parallel corpora. The ranking compo- nent considers not only the overall quality of the rules that produced each candidate, but also the extent to which they preserve gram- maticality and meaning in the particular con- text of the input sentence, as well as the de- gree to which the candidate differs from the input. We experimented with both a Max- imum Entropy classifier and an SVR ranker. Experimental results show that incorporating features from an existing paraphrase recog- nizer in the ranking component improves per- formance, and that our overall method com- pares well against a state of the art paraphrase generator, when paraphrasing rules apply to the input sentences. We also propose a new methodology to evaluate the ranking compo- nents of generate-and-rank paraphrase gener- ators, which evaluates them across different combinations of weights for grammaticality, meaning preservation, and diversity. The pa- per is accompanied by a paraphrasing dataset we constructed for evaluations of this kind. 1 Introduction In recent years, significant effort has been devoted to research on paraphrasing (Androutsopoulos and Malakasiotis, 2010; Madnani and Dorr, 2010). The methods that have been proposed can be roughly classified into three categories: (i) recognition meth- ods, i.e., methods that detect whether or not two in- put sentences or other texts are paraphrases; (ii) gen- eration methods, where the aim is to produce para- phrases of a given input sentence; and (iii) extraction methods, which aim to extract paraphrasing rules (e.g., “X wrote Y ” “↔ Y was authored by X”) or similar patterns from corpora. Most of the methods that have been proposed belong in the first category, possibly because of the thrust provided by related research on textual entailment recognition (Dagan et al., 2009), where the goal is to decide whether or not the information of a given text is entailed by that of another. Significant progress has also been made in paraphrase extraction, where most recent methods produce large numbers of paraphrasing rules from multilingual parallel corpora (Bannard and Callison- Burch, 2005; Callison-Burch, 2008; Zhao et al., 2008; Zhao et al., 2009a; Zhao et al., 2009b; Kok and Brockett, 2010). In this paper, we are concerned with paraphrase generation, which has received less attention than the other two categories. There are currently two main approaches to para- phrase generation. The first one treats paraphrase generation as a machine translation problem, with the peculiarity that the target language is the same as the source one. To bypass the …

Content truncated to 500 words