relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3041/
title: An Attention-Based Long-Short-Term-Memory Model for Paraphrase Generation
creator: Nguyen, Ngoc Khuong
creator: Le, Anh Cuong
creator: Nguyen, Viet Ha
subject: Information Technology (IT)
description: Neural network based sequence-to-sequence models have shown to be the effective approach for paraphrase generation. In the problem of paraphrase generation, there are some words which should be ignored in the target text generation. The current models do not pay enough attention to this problem. To overcome this limitation, in this paper we propose a new model which is a penalty coefficient attention-based Residual Long-Short-Term-Memory (PCA-RLSTM) neural network for forming an end-to-end paraphrase generation model. Extensive experiments on the two most popular corpora (PPDB and WikiAnswers) show that our proposed model’s performance is better than the state-of-the-art models for paragraph generation problem.
date: 2018
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Nguyen, Ngoc Khuong and Le, Anh Cuong and Nguyen, Viet Ha  (2018) An Attention-Based Long-Short-Term-Memory Model for Paraphrase Generation.  In: IUKM 2018, 04 February 2018.     
relation: https://link.springer.com/chapter/10.1007%2F978-3-319-75429-1_14