Towards deep analysis of compositional classes of paraphrases, we have examined a class-oriented framework for collecting paraphrase examples, in which sentential paraphrases are collected for each paraphrase class separately by means of automatic candidate generation and manual judgement. Our preliminary experiments on building a paraphrase corpus have so far been producing promising results, ...

This work, concerning paraphrase identification task, on one hand contributes to expanding deep learning embeddings to include continuous and discontinuous linguistic phrases. On the other hand, it comes up with a new scheme TF-KLD-KNN to learn the discriminative weights of words and phrases specific to paraphrase task, so that a weighted sum of embeddings can represent sentences more effective...

Whenever the text contains multiple ways of saying “the same thing,” but the application requires the same treatment of those various alternatives, an automated paraphrase recognition mechanism would be useful. One reason why paraphrase recognition systems have been difficult to build is because paraphrases are hard to define. Although the strict interpretation of the term “paraphrase” is quite...

We present a novel approach to deciding whether two sentences hold a paraphrase relationship. We employ a generative model that generates a paraphrase of a given sentence, and we use probabilistic inference to reason about whether two sentences share the paraphrase relationship. The model cleanly incorporates both syntax and lexical semantics using quasi-synchronous dependency grammars (Smith a...

A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase...

We present the 1.0 release of our paraphrase database, PPDB. Its English portion, PPDB:Eng, contains over 220 million paraphrase pairs, consisting of 73 million phrasal and 8 million lexical paraphrases, as well as 140 million paraphrase patterns, which capture many meaning-preserving syntactic transformations. The paraphrases are extracted from bilingual parallel corpora totaling over 100 mill...

We introduce a new task of entailment relation aware paraphrase generation which aims at generating conforming to given (e.g. equivalent, forward entailing, or reverse entailing) with respect input. propose reinforcement learning-based weakly-supervised paraphrasing system, ERAP, that can be trained using existing and natural language inference (NLI) corpora without an explicit task-specific co...

This article delves into the scoring function of the statistical paraphrase generation model. It presents an algorithm for exact computation and two applicative experiments. The first experiment analyses the behaviour of a statistical paraphrase generation decoder, and raises some issues with the ordering of n-best outputs. The second experiment shows that a major boost of performance can be ob...

Paraphrase plagiarism is one of the difficult challenges facing plagiarism detection systems. Paraphrasing occur when texts are lexically or syntactically altered to look different, but retain their original meaning. Most plagiarism detection systems (many of which are commercial based) are designed to detect word co-occurrences and light modifications, but are unable to detect severe semantic ...

Paraphrase generation in any language has gained much attention and importance in the study of Natural Language Processing. Therefore, the focus of this paper is on Thai language paraphrase generation for the sentence level. Six sentence paraphrasing techniques for Thai are proposed and illustratively explained. In addition, the Thai–sentence Paraphrase Generation (TPG) system is designed using...