| Challenge: | Existing approaches to generate accurate and different-appearing paraphrases require massive parallel samples for training. |
| Approach: | They propose a novel approach that accomplishes Unsupervised Paraphrasing by Simulated Annealing by performing local editing. |
| Outcome: | The proposed approach outperforms existing models in automatic and human evaluations on Quora, Wikianswers, MSCOCO, and Twitter. |
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| Challenge: | Existing methods for generating paraphrases with linguistic knowledge are often domain specific and hard to scale, or yield inferior results. |
| Approach: | They propose an end-to-end conditional generative architecture for generating paraphrases via adversarial training which does not depend on extra linguistic information. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations on four public datasets. |
Paraphrase Generation as Unsupervised Machine Translation (2022.coling-1)
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| Challenge: | Existing methods for paraphrase generation rely on labeled datasets or are limited in narrow domains. |
| Approach: | They propose a paradigm for paraphrase generation by treating the task as unsupervised machine translation based on pairs of unlabeled monolingual sentences. |
| Outcome: | The proposed paradigm can generate paraphrases on a large unlabeled monolingual corpus without relying on bilingual sentence pairs. |
Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build. |
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ParaMac: A General Unsupervised Paraphrase Generation Framework Leveraging Semantic Constraints and Diversifying Mechanisms (2022.findings-emnlp)
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| Challenge: | Existing unsupervised methods for paraphrase generation are weak in semantic equivalence or expression diversity. |
| Approach: | They propose a framework for unsupervised paraphrase generation that employs multi-aspect equivalence constraints and multi-granularity diversifying mechanisms to achieve good semantic equvalence and expressive diversity. |
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Paraphrase Generation: A Survey of the State of the Art (2021.emnlp-main)
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| Challenge: | Using neural models, paraphrase generation research has shifted to neural methods . a recent study focused on paraphrases, which are used in language understanding tasks . |
| Approach: | They propose to use neural methods to generate fluent, diverse paraphrases from a sentence . they propose to combine large pretrained language models with other mechanisms to generate more advanced paraphrase generation models. |
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Unsupervised Paraphrasing with Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Paraphrase generation has benefited from recent advances in the design of training objectives and model architectures, but previous studies focused on supervised methods that require a large amount of labeled data that is costly to collect. |
| Approach: | They propose a transfer learning approach that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. |
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ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)
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| Challenge: | Paraphrase generation is a long-standing task in natural language processing (NLP). |
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Semi-Supervised Learning for Neural Keyphrase Generation (D18-1)
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| Challenge: | Existing models for keyphrase generation only use labeled data, which is limited to resource-rich domains. |
| Approach: | They propose semi-supervised keyphrase generation methods by leveraging labeled data and large-scale unlabeled samples for learning. |
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Comparative Study of Sentence Embeddings for Contextual Paraphrasing (2020.lrec-1)
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| Challenge: | Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented. |
| Approach: | They propose to use contextual paraphrasing to capture the meaning of a sentence while performing dialogue act clustering. |
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Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
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| Challenge: | Lexically diverse paraphrases are crucial in data augmentation because they enhance the linguistic diversity of the corpus. |
| Approach: | They propose a controllable model for semantic and lexical similarities by attaching tags to the head of the input sentence. |
| Outcome: | The proposed model can paraphrase an input sentence according to the tags specified. |