Papers with paraphrase models
Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap (2023.starsem-1)
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| Challenge: | Existing models for paraphrase detection are not suitable for many applications . existing datasets ignore and fail to test models in this setup . |
| Approach: | They propose to use adversarial paradigms to test paraphrase detection models . they propose to examine the sensitivity to different degrees of semantic overlap . |
| Outcome: | Empirical results show that paraphrase models and different sentence encoders appear successful on evaluations, but measuring the degree of semantic overlap remains a big challenge for them. |
A Comparison of Two Paraphrase Models for Taxonomy Augmentation (N18-2)
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| Challenge: | a taxonomy is often used to look up concepts in text documents. |
| Approach: | They compare two state-of-the-art paraphrase models with a paraphrase dataset . they find that paraphrasing is a viable method to augment taxonomies with more terms . |
| Outcome: | The proposed model outperforms the previous model on the risk domain. |
A Natural Approach for Synthetic Short-Form Text Analysis (2024.lrec-main)
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| Challenge: | Social media and news sites can be flooded with synthetically generated misinformation via tweets and posts while authentic users can inadvertently spread this text via shares and retweets. |
| Approach: | They propose a method of detecting synthetically generated tweets via a Transformer architecture and incorporate unique style-based features. |
| Outcome: | The proposed method detects synthetically generated tweets using a Transformer architecture and incorporating unique style-based features. |
Mitigating Paraphrase Attacks on Machine-Text Detection via Paraphrase Inversion (2025.findings-acl)
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| Challenge: | Paraphrases applied to machine-generated texts can degrade performance of machine-text detectors. |
| Approach: | They propose an approach which frames the problem as translation from paraphrased text back to the original text. |
| Outcome: | The proposed approach yields an average improvement of +22% AUROC across seven detectors and three different domains. |
Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory (2020.findings-emnlp)
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| Challenge: | Having high quality annotated data is crucial for training supervised machine learning models. |
| Approach: | They propose automated methods to improve NLP datasets by viewing them as graphs with expected semantic properties. |
| Outcome: | The proposed methods improve paraphrase models on pre-trained datasets. |
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)
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| Challenge: | Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair. |
| Approach: | They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence. |
| Outcome: | The proposed dataset can be used to train an automatic scorer for language generation evaluation. |
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. |