most citedSciFive: a text-to-text transformer model for biomedical literature

94 citations · 106 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL20214 cited

VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?

Hieu Nguyen, Long Phan, James Anibal +2

Text summarization is a challenging task within natural language processing that involves text generation from lengthy input sequences. While this task has been widely studied in E…

cs.CL2021

SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs

Hieu Tran, Long Phan, James Anibal +2

In this paper, we propose SPBERT, a transformer-based language model pre-trained on massive SPARQL query logs. By incorporating masked language modeling objectives and the word str…

cs.AI20217 cited

CoTexT: Multi-task Learning with Code-Text Transformer

Long Phan, Hieu Tran, Daniel Le +4

We present CoTexT, a pre-trained, transformer-based encoder-decoder model that learns the representative context between natural language (NL) and programming language (PL). Using…

cs.CL202194 cited

SciFive: a text-to-text transformer model for biomedical literature

Long N. Phan, James T. Anibal, Hieu Tran +4

In this report, we introduce SciFive, a domain-specific T5 model that has been pre-trained on large biomedical corpora. Our model outperforms the current SOTA methods (i.e. BERT, B…

cs.CL2021

Hierarchical Transformer Encoders for Vietnamese Spelling Correction

Hieu Tran, Cuong V. Dinh, Long Phan +1

In this paper, we propose a Hierarchical Transformer model for Vietnamese spelling correction problem. The model consists of multiple Transformer encoders and utilizes both charact…

cs.CL20201 cited

Leveraging Transfer Learning for Reliable Intelligence Identification on Vietnamese SNSs (ReINTEL)

Trung-Hieu Tran, Long Phan, Truong-Son Nguyen +1

This paper proposed several transformer-based approaches for Reliable Intelligence Identification on Vietnamese social network sites at VLSP 2020 evaluation campaign. We exploit bo…