A precise and commonly accepted definition of paraphrasing does not exist. This is one of the reasons that have prevented computational linguistics from a real success when dealing with this phenomenon in its systems and applications. With the aim of helping to overcome this difficulty, in this article, new insights on paraphrase characterization are provided. We first overview what has been sa...

We release the Simple Paraphrase Database, a subset of of the Paraphrase Database (PPDB) adapted for the task of text simplification. We train a supervised model to associate simplification scores with each phrase pair, producing rankings competitive with state-of-theart lexical simplification models. Our new simplification database contains 4.5 million paraphrase rules, making it the largest a...

We explore using recursive autoencoders for SemEval 2015 Task 1: Paraphrase and Semantic Similarity in Twitter. Our paraphrase detection system makes use of phrase-structure parse tree embeddings that are then provided as input to a conventional supervised classification model. We achieve an F1 score of 0.45 on paraphrase identification and a Pearson correlation of 0.303 on computing semantic s...

We release the Simple Paraphrase Database, a subset of of the Paraphrase Database (PPDB) adapted for the task of text simplification. We train a supervised model to associate simplification scores with each phrase pair, producing rankings competitive with state-of-theart lexical simplification models. Our new simplification database contains 4.4 million paraphrase rules, making it the largest a...

First the notion "paraphrase" is defined, and then several different types of paraphrase are analyzed: transformational, attenuated, lexical, deriva-tional, and real-world. Next, several different methods of retrieving information are discussed utilizing the notions of paraphrase defined previously. It is concluded that a combination keyword-keyphrase method would constitute the optimum procedure.

Semantic interaction between text segments, which has been proven to be very useful for detecting the paraphrase relations, is often ignored in the study of paraphrase identification. In this paper, we adopt a neural network model for paraphrase identification, called as bidirectional Long Short-Term Memory-Gated Relevance Network (BiLSTM+GRN). According to this model, a gated relevance network...

The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates. However, it is still unclear how it can best be used, due to the heuristic nature of the confidences and its necessarily incomplete coverage. We propose models to leverage the phrase pairs from the PPDB to build parametric parap...

Paraphrases and paraphrasing algorithms have been found of great importance in various natural language processing tasks. While most paraphrase extraction approaches extract equivalent sentences, sentences are an inconvenient unit for further processing, because they are too specific, and often not exact paraphrases. Paraphrase fragment extraction is a technique that post-processes sentential p...

This paper describes our submission to the WMT10 Shared Evaluation Task and MetricsMATR10. We present a version of the METEOR-NEXT metric with paraphrase tables for five target languages. We describe the creation of these paraphrase tables and conduct a tuning experiment that demonstrates consistent improvement across all languages over baseline versions of the metric without paraphrase resources.