TY  - CONF
ID  - SisLab3041
UR  - https://link.springer.com/chapter/10.1007%2F978-3-319-75429-1_14
A1  - Nguyen, Ngoc Khuong
A1  - Le, Anh Cuong
A1  - Nguyen, Viet Ha
Y1  - 2018///
N2  - 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.
TI  - An Attention-Based Long-Short-Term-Memory Model for Paraphrase Generation
SP  - 166
AV  - none
EP  - 178
T2  - IUKM 2018
ER  -