ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation
This addresses the problem of limited paraphrase resources for scientific text, enabling better natural language processing applications in academia, though it is incremental as it extends existing paraphrase dataset efforts to a new domain.
The authors tackled the lack of large-scale paraphrase datasets in the scientific domain by creating ParaSCI, which includes over 350,000 paraphrase pairs from ACL and arXiv, achieving satisfactory results in human evaluation and downstream tasks like long paraphrase generation.
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.