@inproceedings{ding-etal-2021-learning,
title = "Learning to Selectively Learn for Weakly-supervised Paraphrase Generation",
author = "Ding, Kaize and
Li, Dingcheng and
Li, Alexander Hanbo and
Fan, Xing and
Guo, Chenlei and
Liu, Yang and
Liu, Huan",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.480",
doi = "10.18653/v1/2021.emnlp-main.480",
pages = "5930--5940",
abstract = "Paraphrase generation is a longstanding NLP task that has diverse applications on downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have been proposed to alleviate this issue, they may fail to generate meaningful paraphrases due to the lack of supervision signals. In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with data of weak supervision. Specifically, we tackle the weakly-supervised paraphrase generation problem by: (1) obtaining abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion; and (2) developing a meta-learning framework to progressively select valuable samples for fine-tuning a pre-trained language model BART on the sentential paraphrasing task. We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts.",
}
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<abstract>Paraphrase generation is a longstanding NLP task that has diverse applications on downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have been proposed to alleviate this issue, they may fail to generate meaningful paraphrases due to the lack of supervision signals. In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with data of weak supervision. Specifically, we tackle the weakly-supervised paraphrase generation problem by: (1) obtaining abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion; and (2) developing a meta-learning framework to progressively select valuable samples for fine-tuning a pre-trained language model BART on the sentential paraphrasing task. We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts.</abstract>
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%0 Conference Proceedings
%T Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
%A Ding, Kaize
%A Li, Dingcheng
%A Li, Alexander Hanbo
%A Fan, Xing
%A Guo, Chenlei
%A Liu, Yang
%A Liu, Huan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F ding-etal-2021-learning
%X Paraphrase generation is a longstanding NLP task that has diverse applications on downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have been proposed to alleviate this issue, they may fail to generate meaningful paraphrases due to the lack of supervision signals. In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with data of weak supervision. Specifically, we tackle the weakly-supervised paraphrase generation problem by: (1) obtaining abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion; and (2) developing a meta-learning framework to progressively select valuable samples for fine-tuning a pre-trained language model BART on the sentential paraphrasing task. We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts.
%R 10.18653/v1/2021.emnlp-main.480
%U https://aclanthology.org/2021.emnlp-main.480
%U https://doi.org/10.18653/v1/2021.emnlp-main.480
%P 5930-5940
Markdown (Informal)
[Learning to Selectively Learn for Weakly-supervised Paraphrase Generation](https://aclanthology.org/2021.emnlp-main.480) (Ding et al., EMNLP 2021)
ACL
- Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, and Huan Liu. 2021. Learning to Selectively Learn for Weakly-supervised Paraphrase Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5930–5940, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.