Improving Large-scale Paraphrase Acquisition and Generation
Improving Large-scale Paraphrase Acquisition and Generation
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DOI：
10.48550/arxiv.2210.03235
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发表时间：
2022-10
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影响因子：
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通讯作者：
Yao Dou;Chao Jiang;Wei Xu
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文献类型：
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作者：
Yao Dou;Chao Jiang;Wei Xu
本文讨论了现有基于Twitter的释义数据集中的质量问题，并讨论了使用两种不同的释义定义来识别和生成任务的必要性。我们在Twitter(MultiPIT)语料库中提出了一个新的多主题复述，该复述由总共13万个具有众包(MultiPIT_Crowd)和专家(MultiPIT_Expert)注释的句子对组成，使用两种不同的复述定义来识别复述，此外还包括一个多参考测试集(MultiPIT_NMR)和一个自动构建的大型训练集(MultiPIT_Auto)。通过改进的数据标注质量和特定于任务的释义定义，在我们的数据集上微调的最佳预训练语言模型达到了84.2F1的最先进性能，用于自动释义识别。此外，我们的实验结果还表明，与在Quora、MSCOCO和ParaNMT等其他语料库上微调的对应模型相比，在MultiPIT_Auto上训练的复述生成模型生成的复述更加多样化和高质量。
This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identification and generation tasks. We present a new Multi-Topic Paraphrase in Twitter (MultiPIT) corpus that consists of a total of 130k sentence pairs with crowdsoursing (MultiPIT_crowd) and expert (MultiPIT_expert) annotations using two different paraphrase definitions for paraphrase identification, in addition to a multi-reference test set (MultiPIT_NMR) and a large automatically constructed training set (MultiPIT_Auto) for paraphrase generation. With improved data annotation quality and task-specific paraphrase definition, the best pre-trained language model fine-tuned on our dataset achieves the state-of-the-art performance of 84.2 F1 for automatic paraphrase identification. Furthermore, our empirical results also demonstrate that the paraphrase generation models trained on MultiPIT_Auto generate more diverse and high-quality paraphrases compared to their counterparts fine-tuned on other corpora such as Quora, MSCOCO, and ParaNMT.