Paraphrase Generation
61 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
COCO
GLUE
Quora Question Pairs
MSCOCO
PARANMT-50M
Duolingo STAPLE Shared Task
StyleKQC
Most implemented papers
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator
dev-chauhan/PQG-pytorch
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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.
Paraphrase Generation with Latent Bag of Words
FranxYao/Deep-Generative-Models-for-Natural-Language-Processing
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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.
Neural Syntactic Preordering for Controlled Paraphrase Generation
tagoyal/sow-reap-paraphrasing
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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.
Syntax-guided Controlled Generation of Paraphrases
malllabiisc/SGCP
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TACL 2020
In these methods, syntactic-guidance is sourced from a separate exemplar sentence.
Neural Paraphrase Generation with Stacked Residual LSTM Networks
pushpendughosh/Stock-market-forecasting
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COLING 2016
To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation.
A Deep Generative Framework for Paraphrase Generation
arvind385801/paraphrasegen
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In this paper, we address the problem of generating paraphrases automatically.
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
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.
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
badripatro/PQG
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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.
