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
Most implemented papers
Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model
We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy).
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator
One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far.
Generating Sentences from Disentangled Syntactic and Semantic Spaces
In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces.
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation
Generating paraphrases from given sentences involves decoding words step by step from a large vocabulary.
Revisiting Paraphrase Question Generator using Pairwise Discriminator
One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far.
Automatic Compilation of Resources for Academic Writing and Evaluating with Informal Word Identification and Paraphrasing System
The aim is to build a writing aid system that automatically edits a text so that it better adheres to the academic style of writing.
Simultaneous Translation and Paraphrase for Language Education
We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE).
Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity
Recent work has shown that a multilingual neural machine translation (NMT) model can be used to judge how well a sentence paraphrases another sentence in the same language (Thompson and Post, 2020); however, attempting to generate paraphrases from such a model using standard beam search produces trivial copies or near copies.
iNLTK: Natural Language Toolkit for Indic Languages
We present iNLTK, an open-source NLP library consisting of pre-trained language models and out-of-the-box support for Data Augmentation, Textual Similarity, Sentence Embeddings, Word Embeddings, Tokenization and Text Generation in 13 Indic Languages.
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.