Papers with Paraphrasing
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. |
| Outcome: | The proposed model gains significant diversity and improves quality over state-of-the-art datasets. |
Unsupervised Paraphrasing without Translation (P19-1)
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| Challenge: | Recent work on automatic paraphrasing focuses on methods leveraging machine translation as an intermediate step. |
| Approach: | They propose to learn paraphrasing models only from a monolingual corpus . they propose a residual variant of vector-quantized variational auto-encoder . |
| Outcome: | The proposed model outperforms supervised and unsupervised translation methods in paraphrase identification and training set augmentation. |
StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands (2022.lrec-1)
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| Challenge: | Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner. |
| Approach: | They propose a corpus construction scheme that considers intent and formality of directives in Korean language. |
| Outcome: | The proposed method is validated by a corpus construction scheme on Korean topics. |
Paraphrasing as Zero-shot Translation with Feature-guided Diversity Enhancement (2026.acl-long)
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| Challenge: | Existing studies use parallel corpora for training, which results in less diverse paraphrases. |
| Approach: | They train a bidirectional multilingual neural machine translation model on a bilingual parallel corpus and use it as a paraphrasing model. |
| Outcome: | The proposed method generates paraphrases with higher semantic consistency, literal fluency and sentential diversity than existing parabanks and LLMs. |
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. |
| Outcome: | The proposed task combines paraphrases with dialogue act clustering to capture such contextual paraphrasing. |
PAM: Paraphrase AMR-Centric Evaluation Metric (2025.findings-acl)
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| Challenge: | Current evaluation metrics for paraphrase generation are based on borrowed metrics from text-to-text tasks . this is not ideal for paraphrasing as we typically want variation in the lexicon while persisting semantics. |
| Approach: | They propose a Paraphrase AMR-Centric Evaluation Metric that uses AMR graphs extracted from the input text to evaluate paraphrases. |
| Outcome: | The proposed evaluation metric improves on different semantic textual similarity datasets on paraphrases with human semantic scores. |