Validation of sub-sentential paraphrases acquired from parallel monolingual corpora

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 716–725, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Validation of sub-sentential paraphrases acquired from parallel monolingual corpora Houda Bouamor Aur´elien Max LIMSI-CNRS & Univ. Paris Sud Orsay, France firstname.lastname@limsi.fr Anne Vilnat Abstract The task of paraphrase acquisition from re- lated sentences can be tackled by a variety of techniques making use of various types of knowledge. In this work, we make the hypothesis that their performance can be increased if candidate paraphrases can be validated using information that character- izes paraphrases independently of the set of techniques that proposed them. We imple- ment this as a bi-class classification prob- lem (i.e. paraphrase vs. not paraphrase), allowing any paraphrase acquisition tech- nique to be easily integrated into the com- bination system. We report experiments on two languages, English and French, with 5 individual techniques on parallel mono- lingual parallel corpora obtained via multi- ple translation, and a large set of classifi- cation features including surface to contex- tual similarity measures. Relative improve- ments in F-measure close to 18% are ob- tained on both languages over the best per- forming techniques. 1 Introduction The fact that natural language allows messages to be conveyed in a great variety of ways consti- tutes an important difficulty for NLP, with appli- cations in both text analysis and generation. The term paraphrase is now commonly used in the NLP litterature to refer to textual units of equiva- lent meaning at the phrasal level (including single words). For instance, the phrases six months and half a year form a paraphrase pair applicable in many different contexts, as they would appropri- ately denote the same concept. Although one can envisage to manually build high-coverage lists of synonyms, enumerating meaning equivalences at the level of phrases is too daunting a task for hu- mans. Because this type of knowledge can how- ever greatly benefit many NLP applications, au- tomatic acquisition of such paraphrases has at- tracted a lot of attention (Androutsopoulos and Malakasiotis, 2010; Madnani and Dorr, 2010), and significant research efforts have been devoted to this objective (Callison-Burch, 2007; Bhagat, 2009; Madnani, 2010). Central to acquiring paraphrases is the need of assessing the quality of the candidate paraphrases produced by a given technique. Most works to date have resorted to human evaluation of para- phrases on the levels of grammaticality and mean- ing equivalence. Human evaluation is however often criticized as being both costly and non re- producible, and the situation is even more compli- cated by the inherent complexity of the task that can produce low inter-judge agreement. Task- based evaluation involving the use of paraphras- ing into some application thus seem an acceptable solution, provided the evaluation methodologies for the given task are deemed acceptable. This, in turn, puts the emphasis on observing the im- pact of paraphrasing on the targeted application and is rarely accompanied by a study of the intrin- sic limitations of …

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