| 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. |
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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. |
| Outcome: | This paper examines various approaches to paraphrase generation with a main focus on neural methods. |
DivGAN: Towards Diverse Paraphrase Generation via Diversified Generative Adversarial Network (2020.findings-emnlp)
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| Challenge: | Paraphrases refer to texts that convey the same meaning with different expression forms. |
| Approach: | They propose to incorporate a diversity loss term into a deep generative model to generate diverse paraphrases. |
| Outcome: | The proposed model can generate more diverse paraphrases compared with baselines. |
Paraphrase Generation with Deep Reinforcement Learning (D18-1)
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| Challenge: | Paraphrase generation is an important but challenging task in natural language processing . traditional symbolic approaches to paraphrase generation include rule-based methods, thesaurus-based approaches and statistical machine translation (SMT) |
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Neural-Driven Search-Based Paraphrase Generation (2021.eacl-main)
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| Challenge: | Existing non-supervised paraphrase generation models are biased toward specific problems like question answering or image captioning. |
| Approach: | They propose a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. |
| Outcome: | The proposed algorithms perform well against non-supervised baselines. |
Exploring Diverse Expressions for Paraphrase Generation (D19-1)
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| Challenge: | Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications. |
| Approach: | They propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases. |
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A Preliminary Exploration of GANs for Keyphrase Generation (2020.emnlp-main)
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| Challenge: | Existing studies on extractive keyphrases have shown promising results, but the results suggest that there is room for improvement. |
| Approach: | They propose a new keyphrase generation approach using Generative Adversarial Networks (GANs) their model produces a sequence of keyphrases and a discriminator distinguishes between human-curated and machine-generated keyphrase. |
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Paraphrase Types for Generation and Detection (2023.emnlp-main)
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| Challenge: | Current approaches to paraphrase generation and detection ignore the intricate linguistic properties of language. |
| Approach: | They propose two tasks to consider specific linguistic perturbations at particular text positions. |
| Outcome: | The proposed tasks address the shortcoming of ignoring the linguistic properties of language. |
Negative Lexically Constrained Decoding for Paraphrase Generation (P19-1)
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| Challenge: | Paraphrase generation is a monolingual machine translation problem. |
| Approach: | They propose a neural model that first identifies words in the source sentence that should be paraphrased and then decodes them by negative lexical constraints. |
| Outcome: | The proposed model improves paraphrase generation by making necessary rewrites to an input sentence. |
End-to-end Adversarial Sample Generation for Data Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods for generating adversarial samples have deceived many neural inference models, such as text classification and machine translation. |
| Approach: | They propose an adversarial sample generator that consists of a conditioned paraphrasing model and a condition generator and introduce a pretrained discriminator to help the adversarial sample generator adapt to the data characteristics. |
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Paraphrase Generation and Evaluation on Colloquial-Style Sentences (2020.lrec-1)
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| Challenge: | a new study investigates the quality and novelty of generated paraphrases . paraphrase models can be used for information retrieval and data mining . |
| Approach: | They use state-of-the-art neural machine translation models trained on the Opusparcus corpus to generate paraphrases in six languages. |
| Outcome: | The proposed model outperforms the existing model on human evaluation in five of the six languages. |