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
Most implemented papers
Exemplar-Controllable Paraphrasing and Translation using Bitext
Our experimental results show that our models achieve competitive results on controlled paraphrase generation and strong performance on controlled machine translation.
Reformulating Unsupervised Style Transfer as Paraphrase Generation
martiansideofthemoon/style-transfer-paraphrase
•
•
EMNLP 2020
Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs.
Sound Natural: Content Rephrasing in Dialog Systems
We introduce a new task of rephrasing for a more natural virtual assistant.
Latent Template Induction with Gumbel-CRFs
FranxYao/Gumbel-CRF
•
•
NeurIPS 2020
Learning to control the structure of sentences is a challenging problem in text generation.
ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation
We propose ParaSCI, the first large-scale paraphrase dataset in the scientific field, including 33, 981 paraphrase pairs from ACL (ParaSCI-ACL) and 316, 063 pairs from arXiv (ParaSCI-arXiv).
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs
We also demonstrate that the performance of SynPG is competitive or even better than supervised models when the unannotated data is large.
Factorising Meaning and Form for Intent-Preserving Paraphrasing
tomhosking/separator
•
•
ACL 2021
We propose a method for generating paraphrases of English questions that retain the original intent but use a different surface form.
Neural semi-Markov CRF for Monolingual Word Alignment
chaojiang06/neural-Jacana
•
•
ACL 2021
Monolingual word alignment is important for studying fine-grained editing operations (i. e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc.
Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation
lhryang/crl_egpg
•
•
Findings (EMNLP) 2021
Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence.
Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach
l-zhe/btmpg
•
•
Findings (ACL) 2021
Both automatic and human evaluation show BTmPG can improve the diversity of paraphrase while preserving the semantics of the original sentence.
