Paraphrase Generation
68 papers with code • 3 benchmarks • 16 datasets
Paraphrase Generation involves transforming a natural language sentence to a new sentence, that has the same semantic meaning but a different syntactic or lexical surface form.
Datasets
MS COCO
MSCOCO
Quora Question Pairs
PARANMT-50M
Duolingo STAPLE Shared Task
StyleKQC
Latest papers with no code
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising
This allows for learning a denoising function that operates over both aspects and produces semantically equivalent and syntactically diverse outputs through grounded noise injection.
Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations
In this paper, we demonstrate that leveraging Abstract Meaning Representations (AMR) can greatly improve the performance of unsupervised syntactically controlled paraphrase generation.
Fine-Grained Emotional Paraphrasing along Emotion Gradients
We introduce a new task of fine-grained emotional paraphrasing along emotion gradients, that is, altering the emotional intensities of the paraphrases in fine grain following smooth variations in affective dimensions while preserving the meanings of the originals.
Revision for Concision: A Constrained Paraphrase Generation Task
Academic writing should be concise as concise sentences better keep the readers' attention and convey meaning clearly.
Metaphorical Paraphrase Generation: Feeding Metaphorical Language Models with Literal Texts
This study presents a new approach to metaphorical paraphrase generation by masking literal tokens of literal sentences and unmasking them with metaphorical language models.
Improving Large-scale Paraphrase Acquisition and Generation
This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identification and generation tasks.
CoSe-Co: Text Conditioned Generative CommonSense Contextualizer
To train CoSe-Co, we propose a novel dataset comprising of sentence and commonsense knowledge pairs.
Investigating the use of Paraphrase Generation for Question Reformulation in the FRANK QA system
Our two main conclusions are that cleaning of LC-QuAD 2. 0 is required as the errors present can affect evaluation; and that, due to limitations of FRANK's parser, paraphrase generation is not a method which we can rely on to improve the variety of natural language questions that FRANK can answer.
Understanding Metrics for Paraphrasing
This is not only because of the limitations in text generation capabilities but also due that to the lack of a proper definition of what qualifies as a paraphrase and corresponding metrics to measure how good it is.
Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System
Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time.
