activity
20172022
most citedNatural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks

19 citations · 53 across the 7 of their papers we have counts for

collaborators

16 papers

cs.CL202217 cited

Transformer-based Approaches for Legal Text Processing

Ha-Thanh Nguyen, Minh-Phuong Nguyen, Thi-Hai-Yen Vuong +6

In this paper, we introduce our approaches using Transformer-based models for different problems of the COLIEE 2021 automatic legal text processing competition. Automated processin…

cs.CL2021

HYDRA -- Hyper Dependency Representation Attentions

Ha-Thanh Nguyen, Vu Tran, Tran-Binh Dang +3

Attention is all we need as long as we have enough data. Even so, it is sometimes not easy to determine how much data is enough while the models are becoming larger and larger. In…

cs.CL2021

Sublanguage: A Serious Issue Affects Pretrained Models in Legal Domain

Ha-Thanh Nguyen, Le-Minh Nguyen

Legal English is a sublanguage that is important for everyone but not for everyone to understand. Pretrained models have become best practices among current deep learning approache…

cs.NE2021

SCNN: Swarm Characteristic Neural Network

Ha-Thanh Nguyen, Le-Minh Nguyen

Deep learning is a powerful approach with good performance on many different tasks. However, these models often require massive computational resources. It is a worrying trend that…

cs.CL2020

Improving Multilingual Neural Machine Translation For Low-Resource Languages: French,English - Vietnamese

Thi-Vinh Ngo, Phuong-Thai Nguyen, Thanh-Le Ha +2

Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs' joint training. T…

cs.CL2019

Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation

Thi-Vinh Ngo, Thanh-Le Ha, Phuong-Thai Nguyen +1

Among the six challenges of neural machine translation (NMT) coined by (Koehn and Knowles, 2017), rare-word problem is considered the most severe one, especially in translation of…