论文标题
改善大规模释义和发电
Improving Large-scale Paraphrase Acquisition and Generation
论文作者
论文摘要
本文解决了现有的基于Twitter的释义数据集中的质量问题，并讨论了使用两个单独的释义定义进行识别和生成任务的必要性。 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释义产生。通过改进的数据注释质量和特定于任务的释义定义，我们数据集上的最佳预训练的语言模型可实现84.2 F1的最先进性能，用于自动释义识别。此外，我们的经验结果还表明，与对其他语料库（例如Quora，Mscoco和paranmt）进行微调相比，对Multipit_auto进行训练的释义生成模型会产生更多样化和高质量的释义。
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.
