Paraphrase Generation
66 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
Latest papers
Paraphrase Types for Generation and Detection
Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language.
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase Generation
Furthermore, for situations requiring multiple paraphrases for each source sentence, we design a Diverse Templates Search (DTS) algorithm, which can enhance the diversity between paraphrases without sacrificing quality.
Multilingual Lexical Simplification via Paraphrase Generation
After feeding the input sentence into the encoder of paraphrase modeling, we generate the substitutes based on a novel decoding strategy that concentrates solely on the lexical variations of the complex word.
Explicit Syntactic Guidance for Neural Text Generation
Most existing text generation models follow the sequence-to-sequence paradigm.
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation
Paraphrase generation is a long-standing task in natural language processing (NLP).
PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation
Existing fine-tuning methods for this task are costly as all the parameters of the model need to be updated during the training process.
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing?
CoEdIT: Text Editing by Task-Specific Instruction Tuning
We present a large language model fine-tuned on a diverse collection of task-specific instructions for text editing (a total of 82K instructions).
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various continuous domains.
Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation
Though individual translated texts are often fluent and preserve meaning, at a large scale, translated texts have statistical tendencies which distinguish them from text originally written in the language ("translationese") and can affect model performance.