In this paper we introduce PerPaDa, a Persian paraphrase dataset that is
collected from users' input in a plagiarism detection system. As an implicit
crowdsourcing experience, we have gathered a large collection of original and
paraphrased sentences from Hamtajoo; a Persian plagiarism detection system, in
which users try to conceal cases of text re-use in their documents by
paraphrasing and re-submitting manuscripts for analysis. The compiled dataset
contains 2446 instances of paraphrasing. In order to improve the overall
quality of the collected data, some heuristics have been used to exclude
sentences that don't meet the proposed criteria. The introduced corpus is much
larger than the available datasets for the task of paraphrase identification in
Persian. Moreover, there is less bias in the data compared to the similar
datasets, since the users did not try some fixed predefined rules in order to
generate similar texts to their original inputs.

本文介绍了 PerPaDa，这是一个从用户输入中收集的波斯语近义词语料库，旨在帮助检测抄袭。该语料库包含 2446 个波斯语句子的大量近义词和原句，使用了一些启发式方法来排除不符合标准的句子，相对于类似数据集，该数据集更大且更无偏见。

基于隐式众包数据采集的波斯语释义数据集

PerPaDa: A Persian Paraphrase Dataset based on Implicit Crowdsourcing Data Collection

We propose ParaSCI, the first large-scale paraphrase dataset in the
scientific field, including 33,981 paraphrase pairs from ACL (ParaSCI-ACL) and
316,063 pairs from arXiv (ParaSCI-arXiv). Digging into characteristics and
common patterns of scientific papers, we construct this dataset though
intra-paper and inter-paper methods, such as collecting citations to the same
paper or aggregating definitions by scientific terms. To take advantage of
sentences paraphrased partially, we put up PDBERT as a general paraphrase
discovering method. The major advantages of paraphrases in ParaSCI lie in the
prominent length and textual diversity, which is complementary to existing
paraphrase datasets. ParaSCI obtains satisfactory results on human evaluation
and downstream tasks, especially long paraphrase generation.

提出了 ParaSCI，这是第一个在科学领域内的大规模释义数据集，包括来自 ACL（ParaSCI-ACL）的 33,981 对释义和来自 arXiv（ParaSCI-arXiv）的 316,063 对释义。通过收集对同一篇论文的引用或聚合科学术语的定义等方法，我们挖掘了科学论文的特点和共同模式，并且提出了 PDBERT 作为一种通用的释义发现方法来利用部分释义的句子。ParaSCI 的释义具有突出的长度和文本多样性的优点，这与现有的释义数据集相补充。ParaSCI 在人类评估和下游任务中获得了令人满意的结果，尤其是长篇释义生成方面。

ParaSCI: 一个大型的科学同义改写数据集，用于长句子同义改写生成

ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase  Generation

We present ParaBank, a large-scale English paraphrase dataset that surpasses
prior work in both quantity and quality. Following the approach of ParaNMT, we
train a Czech-English neural machine translation (NMT) system to generate novel
paraphrases of English reference sentences. By adding lexical constraints to
the NMT decoding procedure, however, we are able to produce multiple
high-quality sentential paraphrases per source sentence, yielding an English
paraphrase resource with more than 4 billion generated tokens and exhibiting
greater lexical diversity. Using human judgments, we also demonstrate that
ParaBank's paraphrases improve over ParaNMT on both semantic similarity and
fluency. Finally, we use ParaBank to train a monolingual NMT model with the
same support for lexically-constrained decoding for sentence rewriting tasks.

该论文介绍 ParaBank，这是一个大规模的英语释义数据集，使用神经机器翻译和词汇约束生成英语句子的多个高质量释义版本，可用于句子改写任务。