Sentence simplification reduces semantic complexity to benefit people with language impairments. Previous simplification studies on the sentence level and word level have achieved promising results but also meet great challenges. For sentence-level studies, sentences after simplification are fluent but sometimes are not really simplified. For word-level studies, words are simplified but also have potential grammar errors due to different usages of words before and after simplification. In this paper, we propose a two-step simplification framework by combining both the word-level and the sentence-level simplifications, making use of their corresponding advantages. Based on the two-step framework, we implement a novel constrained neural generation model to simplify sentences given simplified words. The final results on Wikipedia and Simple Wikipedia aligned datasets indicate that our method yields better performance than various baselines.
As for word-level simplification, there are impressive results as well. extract over 30,000 paraphrase rules for lexical simplification by identifying aligned words in English Wikipedia and Simple English Wikipedia. Glava employ GloVe @cite_14 to generate synonyms for the complex words. Instead of using the parallel datasets, their approach only requires a single corpus. propose a new word embeddings model to deal with the limitation that the traditional models do not accommodate ambiguous lexical semantics. Pavlick release about 4,500,000 simple paraphrase rules by extracting normal paraphrases rules from a bilingual corpus and reranking the simplicity scores of these rules by a supervised model. Thanks to their efforts, there is a large number of effective methods for identifying complex words, finding corresponding simple synonyms and selecting qualified substitutions. However, sometimes simplifying complicated words directly with simple synonyms violates grammar rules and usages.
@cite_14
2008127487 2471939052 2752172973 1822046502
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. SemEval 2016 task 11 involved determining whether words in a sentence were complex or simple for a cohort of people with English as a second language. Training data consisted of 200 annotated sentences, representing the combined judgements of 20 human annota- tors, such that if any annotator of the group labelled a word as complex, then it was con- sidered to be complex. Testing was based on single annotator judgements. Our system used a random forest classifier with a variety of features, the most important of which were term frequency statistics garnered from four large corpora, and style lexicons built on two large corpora. Minor features in the final system include the presence or absence of words in various readability word lists; many other features we tried were not successful. Our rank- ing amongst submitted systems did not reflect the strength of our system, due to submitting a far from optimal weighting between complex and simple, but we show that when a more ap- propriate weighting is used, our system ranks amongst the best submitted systems. The success of neural network methods for computing word embeddings has motivated methods for generating semantic embeddings of longer pieces of text, such as sentences and paragraphs. Surprisingly, (ICLR'16) showed that such complicated methods are outperformed, especially in out-of-domain (transfer learning) settings, by simpler methods involving mild retraining of word embeddings and basic linear regression. The method of requires retraining with a substantial labeled dataset such as Paraphrase Database (, 2013). @PARASPLIT The current paper goes further, showing that the following completely unsupervised sentence embedding is a formidable baseline: Use word embeddings computed using one of the popular methods on unlabeled corpus like Wikipedia, represent the sentence by a weighted average of the word vectors, and then modify them a bit using PCA SVD. This weighting improves performance by about 10 to 30 in textual similarity tasks, and beats sophisticated supervised methods including RNN's and LSTM's. It even improves 's embeddings. This simple method should be used as the baseline to beat in future, especially when labeled training data is scarce or nonexistent. @PARASPLIT The paper also gives a theoretical explanation of the success of the above unsupervised method using a latent variable generative model for sentences, which is a simple extension of the model in (TACL'16) with new "smoothing" terms that allow for words occurring out of context, as well as high probabilities for words like and, not in all contexts. We present an approach for automatically learning synonyms from a corpus of paraphrased tweets. The synonyms are learned by using shallow parse chunks to create candidate synonyms and their context windows, and the synonyms are substituted back into a paraphrase detection system that uses machine translation metrics as features for a classifier. We find a 2.29 improvement in F1 when we train and test on the paraphrase training set, demonstrating the importance of discovering high quality synonyms. We also find 9.8 better coverage of the paraphrase corpus using our synonyms rather than larger, existing synonym resources, demonstrating the power of extracting synonyms that are representative of the topics in the test set.