ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation

Qingxiu, Dong,; Wan, Xiaojun; Cao, Yue

doi:10.48550/arxiv.2101.08382

Cited by 1 publication

(1 citation statement)

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“…All pairs are manually labeled to be either paraphrases or nonparaphrases. ParaSCI (Dong, Wan, and Cao 2021) contains 350K automatically extracted paraphrase candidates from ACL and arXiv papers. The extraction heuristics consider term definitions, citation information, and sentence embedding similarity.…”

Section: Paraphrase Datasets For Englishmentioning

confidence: 99%

Towards diverse and contextually anchored paraphrase modeling: A dataset and baselines for Finnish

et al. 2023

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In this paper, we study natural language paraphrasing from both corpus creation and modeling points of view. We focus in particular on the methodology that allows the extraction of challenging examples of paraphrase pairs in their natural textual context, leading to a dataset potentially more suitable for evaluating the models’ ability to represent meaning, especially in document context, when compared with those gathered using various sentence-level heuristics. To this end, we introduce the Turku Paraphrase Corpus, the first large-scale, fully manually annotated corpus of paraphrases in Finnish. The corpus contains 104,645 manually labeled paraphrase pairs, of which 98% are verified to be true paraphrases, either universally or within their present context. In order to control the diversity of the paraphrase pairs and avoid certain biases easily introduced in automatic candidate extraction, the paraphrases are manually collected from different paraphrase-rich text sources. This allows us to create a challenging dataset including longer and more lexically diverse paraphrases than can be expected from those collected through heuristics. In addition to quality, this also allows us to keep the original document context for each pair, making it possible to study paraphrasing in context. To our knowledge, this is the first paraphrase corpus which provides the original document context for the annotated pairs. We also study several paraphrase models trained and evaluated on the new data. Our initial paraphrase classification experiments indicate a challenging nature of the dataset when classifying using the detailed labeling scheme used in the corpus annotation, the accuracy substantially lacking behind human performance. However, when evaluating the models on a large scale paraphrase retrieval task on almost 400M candidate sentences, the results are highly encouraging, 29–53% of the pairs being ranked in the top 10 depending on the paraphrase type. The Turku Paraphrase Corpus is available at github.com/TurkuNLP/Turku-paraphrase-corpus as well as through the popular HuggingFace datasets under the CC-BY-SA license.

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Section: Paraphrase Datasets For Englishmentioning

confidence: 99%