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        <title>Improving Large-scale Paraphrase Acquisition and Generation</title>
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        <namePart type="given">Yao</namePart>
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        <namePart type="given">Chao</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yoav</namePart>
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    <abstract>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.</abstract>
    <identifier type="citekey">dou-etal-2022-improving</identifier>
    <identifier type="doi">10.18653/v1/2022.emnlp-main.631</identifier>
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        <date>2022-12</date>
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%0 Conference Proceedings
%T Improving Large-scale Paraphrase Acquisition and Generation
%A Dou, Yao
%A Jiang, Chao
%A Xu, Wei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F dou-etal-2022-improving
%X 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.
%R 10.18653/v1/2022.emnlp-main.631
%U https://aclanthology.org/2022.emnlp-main.631
%U https://doi.org/10.18653/v1/2022.emnlp-main.631
%P 9301-9323

Markdown (Informal)

[Improving Large-scale Paraphrase Acquisition and Generation](https://aclanthology.org/2022.emnlp-main.631) (Dou et al., EMNLP 2022)

• Improving Large-scale Paraphrase Acquisition and Generation (Dou et al., EMNLP 2022)

ACL

• Yao Dou, Chao Jiang, and Wei Xu. 2022. Improving Large-scale Paraphrase Acquisition and Generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9301–9323, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.