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# Amir Pouran Ben Veyseh

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

Amir Pouran Ben Veyseh 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 [Thien Huu Nguyen](https://md.mova.elamur.ai/free/en/docs/papers/authors/thien-huu-nguyen/), Dejing Dou, Minh Van Nguyen.

[OpenAlex](https://openalex.org/A5027729448) · [ORCID](https://orcid.org/0000-0002-6826-8113) · [**h-index 17**](https://openalex.org/authors/A5027729448) · [2380 citations](https://openalex.org/authors/A5027729448) · [45 works](https://openalex.org/works?filter=author.id:A5027729448) — 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=Amir+Pouran+Ben+Veyseh) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Amir+Pouran+Ben+Veyseh) · [dblp](https://dblp.org/search?q=Amir+Pouran+Ben+Veyseh) · [ACL Anthology](https://aclanthology.org/search/?q=Amir+Pouran+Ben+Veyseh) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Amir+Pouran+Ben+Veyseh)

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)

- [Thien Huu Nguyen](https://md.mova.elamur.ai/free/en/docs/papers/authors/thien-huu-nguyen/) ×2
- Dejing Dou
- Minh Van Nguyen
- Viet Dac Lai
- [← previous Amir More](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-more/)
- [next → Amir Zeldes](https://md.mova.elamur.ai/free/en/docs/papers/authors/amir-zeldes/)
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