Parfrasgenerering

Parfrasgenerering, often called paraphrase generation, is the task of producing rewritten text that preserves the original meaning while altering wording and syntactic structure. It is used in natural language processing to diversify expression, adapt content for different audiences, or enable downstream tasks such as data augmentation and text simplification.

Modern approaches are predominantly neural. Encoder-decoder architectures, especially transformer-based models, are trained on parallel paraphrase data or fine-tuned with objectives that balance semantic preservation and fluent, natural wording. Generation strategies include beam search for higher quality outputs, as well as sampling and controllable generation to adjust formality, length, or tone. Traditional methods include rule-based transformations or statistical models, but these have largely been surpassed by neural methods in performance and flexibility.

Evaluation of paraphrase quality combines automatic and human assessments. Automatic metrics such as BLEU, ROUGE, and METEOR measure lexical overlap and n-gram similarity, while newer semantic metrics like BERTScore aim to capture meaning. Human evaluation remains important for judgment of fluency, adequacy, and stylistic appropriateness. Challenges include preserving the exact meaning in all contexts, ensuring grammaticality and naturalness, avoiding repetition, and controlling stylistic attributes without degrading content.

Applications span data augmentation for natural language processing tasks, text simplification, style transfer, and content rewriting for readability or localization. Parafrasing also supports misinformation risk reduction, educational tools for language learning, and aiding in plagiarism-aware paraphrase detection.

Ethical and legal considerations address misuse, copyright concerns, and potential bias or harmful content. Responsible use emphasizes quality monitoring, transparency about generated content, and adherence to applicable licenses and attribution requirements.