Improving Large-scale Paraphrase Acquisition and Generation

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9301 - 9323 December 7-11, 2022 ©2022 Association for Computational Linguistics Improving Large-scale Paraphrase Acquisition and Generation Yao Dou, Chao Jiang, Wei Xu School of Interactive Computing Georgia Institute of Technology {douy, chaojiang}@gatech.edu; wei.xu@cc.gatech.edu http://twitter-paraphrase.com/ Abstract This paper addresses the quality issues in ex- isting 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 (MULTIPITCROWD) and expert (MULTIPITEXPERT) annotations using two dif- ferent paraphrase definitions for paraphrase identification, in addition to a multi-reference test set (MULTIPITNMR) and a large automati- cally constructed training set (MULTIPITAUTO) 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 auto- matic paraphrase identification. Furthermore, our empirical results also demonstrate that the paraphrase generation models trained on MUL- TIPITAUTO generate more diverse and high- quality paraphrases compared to their counter- parts fine-tuned on other corpora such as Quora, MSCOCO, and ParaNMT. 1 Introduction Paraphrases are alternative expressions that con- vey a similar meaning (Bhagat and Hovy, 2013). Studying paraphrase facilitates research in both nat- ural language understanding and generation. For instance, identifying paraphrases on social media is important for tracking the spread of misinforma- tion (Bakshy et al., 2011) and capturing emerging events (Vosoughi and Roy, 2016). On the other hand, paraphrase generation improves the linguistic diversity in conventional agents (Li et al., 2016) and machine translation (Thompson and Post, 2020). It has also been successfully applied in data argumen- tation to improve information extraction (Zhang et al., 2015; Ferguson et al., 2018) and question answering systems (Gan and Ng, 2019). 7. In Tibet, climate change causes bigger, faster avalanches. 1. Bigger, faster collapsing glaciers, triggered by climate change 2. Bigger, Faster Avalanches, Trigg- ered by Climate Change in Tibet 8. Bigger, faster avalanches in Tibet, triggered by climate change. 9. @KendraWrites on a study that showed climate change drove cataclysmic avalanches in Tibet Formal Similar 3. It's finally Friday I'm so happiiiiiii. 22. yayayayayyaya it's finally Friday and I have a half day today 20. I'm so happy it's finally Friday duck yeah 21. I've never been so happy that it's finally Friday Informal Diverse 1. it's finally Friday and that's all that matters rn. 2. So so so so thankful it's finally Friday. 2,929 #FinallyFriday 6 Trending 11.2K Tweets #FinallyFriday 6 Trending 11.2K Tweets #FinallyFriday 6 Trending 11.2K Tweets #FinallyFriday 6 Trending 11.2K Tweets nytime.com Bigger, Faster, Avalanches, Triggered … A deadly 2016 glacier collapse in Tibet … nytime.com Bigger, Faster, Avalanches, Triggered … A deadly 2016 glacier collapse in Tibet … nytime.com Bigger, Faster, Avalanches, Triggered … A deadly 2016 glacier collapse in Tibet … https://www.nytimes.com/2018/01/23/climate… nytime.com Bigger, Faster, Avalanches, Triggered … A deadly 2016 glacier collapse in …

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