Paraphrase Generation

35 papers with code • 2 benchmarks • 11 datasets

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Greatest papers with code

iNLTK: Natural Language Toolkit for Indic Languages

goru001/inltk EMNLP (NLPOSS) 2020

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.

Data Augmentation Paraphrase Generation +4

Paraphrase Generation with Latent Bag of Words

FranxYao/Deep-Generative-Models-for-Natural-Language-Processing NeurIPS 2019

Inspired by variational autoencoders with discrete latent structures, in this work, we propose a latent bag of words (BOW) model for paraphrase generation.

Paraphrase Generation Word Embeddings

Neural Paraphrase Generation with Stacked Residual LSTM Networks

pushpendughosh/Stock-market-forecasting COLING 2016

To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation.

Paraphrase Generation Sentence Similarity

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.

Fine-tuning Paraphrase Generation +1

Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation

lancopku/WEAN NAACL 2018

The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words.

Abstractive Text Summarization Paraphrase Generation +2

Neural Syntactic Preordering for Controlled Paraphrase Generation

tagoyal/sow-reap-paraphrasing ACL 2020

Paraphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization.

Machine Translation Paraphrase Generation +1

Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity

thompsonb/prism WMT (EMNLP) 2020

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.

Machine Translation Paraphrase Generation +3

Syntax-guided Controlled Generation of Paraphrases

malllabiisc/SGCP TACL 2020

In these methods, syntactic-guidance is sourced from a separate exemplar sentence.

Paraphrase Generation

Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator

badripatro/PQG COLING 2018

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.

Machine Reading Comprehension Machine Translation +3