@inproceedings{chen-etal-2022-mcpg,
title = "{MCPG}: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation",
author = "Chen, Yi and
Jiang, Haiyun and
Liu, Lemao and
Wang, Rui and
Shi, Shuming and
Xu, Ruifeng",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.439",
doi = "10.18653/v1/2022.findings-emnlp.439",
pages = "5948--5958",
abstract = "We present MCPG: a simple and effectiveapproach for controllable unsupervised paraphrase generation, which is also flexible toadapt to specific domains without extra training. MCPG is controllable in different levels: local lexicons, global semantics, and universal styles. The unsupervised paradigm ofMCPG combines factual keywords and diversified semantic embeddings as local lexical andglobal semantic constraints. The semantic embeddings are diversified by standard dropout,which is exploited for the first time to increaseinference diversity by us. Moreover, MCPGis qualified with good domain adaptability byadding a transfer vector as a universal style constraint, which is refined from the exemplars retrieved from the corpus of the target domain in atraining-free way. Extensive experiments showthat MCPG outperforms state-of-the-art unsupervised baselines by a margin. Meanwhile,our domain-adapted MCPG also achieves competitive performance with strong supervisedbaselines even without training.",
}
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%0 Conference Proceedings
%T MCPG: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation
%A Chen, Yi
%A Jiang, Haiyun
%A Liu, Lemao
%A Wang, Rui
%A Shi, Shuming
%A Xu, Ruifeng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F chen-etal-2022-mcpg
%X We present MCPG: a simple and effectiveapproach for controllable unsupervised paraphrase generation, which is also flexible toadapt to specific domains without extra training. MCPG is controllable in different levels: local lexicons, global semantics, and universal styles. The unsupervised paradigm ofMCPG combines factual keywords and diversified semantic embeddings as local lexical andglobal semantic constraints. The semantic embeddings are diversified by standard dropout,which is exploited for the first time to increaseinference diversity by us. Moreover, MCPGis qualified with good domain adaptability byadding a transfer vector as a universal style constraint, which is refined from the exemplars retrieved from the corpus of the target domain in atraining-free way. Extensive experiments showthat MCPG outperforms state-of-the-art unsupervised baselines by a margin. Meanwhile,our domain-adapted MCPG also achieves competitive performance with strong supervisedbaselines even without training.
%R 10.18653/v1/2022.findings-emnlp.439
%U https://aclanthology.org/2022.findings-emnlp.439
%U https://doi.org/10.18653/v1/2022.findings-emnlp.439
%P 5948-5958
Markdown (Informal)
[MCPG: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation](https://aclanthology.org/2022.findings-emnlp.439) (Chen et al., Findings 2022)
ACL