ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation
ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation
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ParaSCI：用于更长释义生成的大型科学释义数据集
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Yue Cao
中科院分区：
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作者：
Qingxiu Dong;Xiaojun Wan;Yue Cao
我们提出了ParaSCI，这是科学领域第一个大规模的释义数据集，包括来自ACL的33，981个释义对（ParaSCI-ACL）和来自arXiv的316，063个释义对（ParaSCI-arXiv）。通过深入挖掘科技论文的特点和共同模式，采用论文内和论文间的方法构建数据集，如收集同一篇论文的引文或按科学术语聚合定义。为了利用部分释义的句子，我们提出了PDBERT作为通用的释义发现方法。ParaSCI中释义的主要优势在于显著的长度和文本的多样性，这是对现有释义数据集的补充。ParaSCI在人工评价和下游任务上取得了令人满意的结果，特别是长释义生成。
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.