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 identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at 70 precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.
Researchers have found several data sources from which to collect sentential paraphrases: multiple news agencies reporting the same event (MSRP) @cite_37 @cite_5 , multiple translated versions of a foreign novel @cite_28 @cite_53 or other texts @cite_42 , multiple definitions of the same concept @cite_22 , descriptions of the same video clip from multiple workers @cite_48 or rephrased sentences @cite_9 @cite_43 . However, all these data collection methods are incapable of obtaining sentential paraphrases on a large scale (i.e. limited number of news agencies or books with multiple translated versions), and or lack meaningful negative examples. Both of these properties are crucial for developing machine learning models that identify paraphrases and measure semantic similarities.
@cite_37 @cite_22 @cite_28 @cite_48 @cite_53 @cite_42 @cite_9 @cite_43 @cite_5
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We consider the problem of learning general-purpose, paraphrastic sentence embeddings in the setting of (2016b). We use neural machine translation to generate sentential paraphrases via back-translation of bilingual sentence pairs. We evaluate the paraphrase pairs by their ability to serve as training data for learning paraphrastic sentence embeddings. We find that the data quality is stronger than prior work based on bitext and on par with manually-written English paraphrase pairs, with the advantage that our approach can scale up to generate large training sets for many languages and domains. We experiment with several language pairs and data sources, and develop a variety of data filtering techniques. In the process, we explore how neural machine translation output differs from human-written sentences, finding clear differences in length, the amount of repetition, and the use of rare words. In this paper, we propose to enhance machine translation system combination (MTSC) with a sentence-level paraphrasing model trained by a neural network. This work extends the number of candidates in MTSC by paraphrasing the whole original MT translation sentences. First we train a neural paraphrasing model of Encoder-Decoder, and leverage the model to paraphrase the MT system outputs to generate synonymous candidates in the semantic space. Then we merge all of them into a single improved translation by a state-of-the-art system combination approach (MEMT) adding some new paraphrasing features. Our experimental results show a significant improvement of 0.28 BLEU points on the WMT2011 test data and 0.41 BLEU points without considering the out-of-vocabulary (OOV) words for the sentence-level paraphrasing model. To paraphrase means to rewrite content while preserving the original meaning. Paraphrasing is important in fields such as text reuse in journalism, anonymizing work, and improving the quality of customer-written reviews. This article contributes to paraphrase acquisition and focuses on two aspects that are not addressed by current research: (1) acquisition via crowdsourcing, and (2) acquisition of passage-level samples. The challenge of the first aspect is automatic quality assurance; without such a means the crowdsourcing paradigm is not effective, and without crowdsourcing the creation of test corpora is unacceptably expensive for realistic order of magnitudes. The second aspect addresses the deficit that most of the previous work in generating and evaluating paraphrases has been conducted using sentence-level paraphrases or shorter; these short-sample analyses are limited in terms of application to plagiarism detection, for example. We present the Webis Crowd Paraphrase Corpus 2011 (Webis-CPC-11), which recently formed part of the PAN 2010 international plagiarism detection competition. This corpus comprises passage-level paraphrases with 4067 positive samples and 3792 negative samples that failed our criteria, using Amazon's Mechanical Turk for crowdsourcing. In this article, we review the lessons learned at PAN 2010, and explain in detail the method used to construct the corpus. The empirical contributions include machine learning experiments to explore if passage-level paraphrases can be identified in a two-class classification problem using paraphrase similarity features, and we find that a k-nearest-neighbor classifier can correctly distinguish between paraphrased and nonparaphrased samples with 0.980 precision at 0.523 recall. This result implies that just under half of our samples must be discarded (remaining 0.477 fraction), but our cost analysis shows that the automation we introduce results in a 18p financial saving and over 100 hours of time returned to the researchers when repeating a similar corpus design. On the other hand, when building an unrelated corpus requiring, say, 25p training data for the automated component, we show that the financial outcome is cost neutral, while still returning over 70 hours of time to the researchers. The work presented here is the first to join the paraphrasing and plagiarism communities. A lack of standard datasets and evaluation metrics has prevented the field of paraphrasing from making the kind of rapid progress enjoyed by the machine translation community over the last 15 years. We address both problems by presenting a novel data collection framework that produces highly parallel text data relatively inexpensively and on a large scale. The highly parallel nature of this data allows us to use simple n-gram comparisons to measure both the semantic adequacy and lexical dissimilarity of paraphrase candidates. In addition to being simple and efficient to compute, experiments show that these metrics correlate highly with human judgments.