[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/authors/thien-huu-nguyen/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/) / Thien Huu Nguyen

# Thien Huu Nguyen

2 paper cards in Mova's library · 2019–2021

Thien Huu Nguyen wrote 2 of the papers Mova reads, in 2019–2021, most of them on [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/); often with [Amir Pouran Ben Veyseh](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-pouran-ben-veyseh/), Dejing Dou, Minh Van Nguyen.

[OpenAlex](https://openalex.org/A5026113034) · [ORCID](https://orcid.org/0000-0003-3768-4736) · [**h-index 39**](https://openalex.org/authors/A5026113034) · [7682 citations](https://openalex.org/authors/A5026113034) · [228 works](https://openalex.org/works?filter=author.id:A5026113034) — openalex, 2026-10-18

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Thien+Huu+Nguyen) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Thien+Huu+Nguyen) · [dblp](https://dblp.org/search?q=Thien+Huu+Nguyen) · [ACL Anthology](https://aclanthology.org/search/?q=Thien+Huu+Nguyen) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Thien+Huu+Nguyen)

Topics: [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/) 1

## Papers

1. **[Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing](https://md.mova.elamur.ai/free/en/docs/papers/ud-2021-nguyen-trankit-light-weight-transformer-based/)** *(2021)* Trankit is a toolkit for many languages built on one shared XLM-RoBERTa encoder, with small language-specific adapters plugged into each layer, so that each language or task changes only its adapter and its output layers.
2. **[Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures](https://md.mova.elamur.ai/free/en/docs/papers/veyseh-2019-graph-based-neural-networks-event/)** *(2019)* Event factuality prediction gives a real score in [−3, +3] for an event mention. Cue words that set the factuality can be far from the event word in the sentence, and a dependency tree can reach them while a sequential model can weigh them by meaning. The model has three parts: BERT and two BiLSTM layers for the…

## Co-authors (4)

- [Amir Pouran Ben Veyseh](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-pouran-ben-veyseh/) ×2
- Dejing Dou
- Minh Van Nguyen
- Viet Dac Lai
- [← previous Thiago Castro Ferreira](https://md.mova.elamur.ai/free/en/docs/papers/authors/thiago-castro-ferreira/)
- [next → Thomas Hallgren](https://md.mova.elamur.ai/free/en/docs/papers/authors/thomas-hallgren/)
- [↑ back to Authors](https://md.mova.elamur.ai/free/en/docs/papers/authors/)