Papers by Chien Nguyen
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code (2025.coling-industry)
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Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Vu Minh Chien, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Barbosa Junior, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Nour Moustafa-Fahmy, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Hiep Nguyen, Sampo Pyysalo
| Challenge: | Pretrained language models are integral part of AI applications, but their high computational cost limits accessibility. |
| Approach: | They evaluate Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. |
| Outcome: | The proposed model outperforms existing models on English, Finnish, Hindi, Japanese, Vietnamese, and code. |
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback (2023.emnlp-demo)
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| Challenge: | Existing instruction-tuned open-source LLMs have only been instruction- tuned for English and a few popular languages, thus hindering their accessibility to many other languages in the world. |
| Approach: | They propose a framework that uses supervised fine-tuning and reinforcement learning from human feedback to improve the accessibility of large language models. |
| Outcome: | The proposed framework enables the evaluation of generative LLMs in multiple languages. |
MCECR: A Novel Dataset for Multilingual Cross-Document Event Coreference Resolution (2024.findings-naacl)
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| Challenge: | Existing datasets for event coreference resolution focus on within-document event coreference and English text, lacking cross-document ECR datasets beyond English. |
| Approach: | They propose a multiligual dataset that manually annotates documents for event mentions and coreference in five languages. |
| Outcome: | The proposed dataset annotates documents for event mentions and coreference in five languages . the dataset fetches related news articles from the google search engine to increase the number of non-singleton clusters . |
Contextualized Soft Prompts for Extraction of Event Arguments (2023.findings-acl)
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| Challenge: | Existing prompt-based methods for event argument extraction rely on discrete and manually-designed prompts that cannot exploit specific context for each example. |
| Approach: | They propose a prompt-based method that introduces soft prompts to facilitate encoding of individual example context and multiple relevant documents to boost EAE. |
| Outcome: | The proposed method extensively evaluates on benchmark datasets to demonstrate its benefits with state-of-the-art performance. |
Transitioning Representations between Languages for Cross-lingual Event Detection via Langevin Dynamics (2023.findings-emnlp)
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| Challenge: | Existing datasets for event detection (ED) are limited to a small set of popular languages due to the high cost of data annotation. |
| Approach: | They propose a method to develop cross-lingual transfer learning models in high-resource source languages . they aim to transition the representations for target-language examples into the source-language space . |
| Outcome: | Extensive experiments over three languages demonstrate the state-of-the-art in CLTL. |
A Spectral Viewpoint on Continual Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing methods to solve the Continual Relation Extraction problem have been proposed . |
| Approach: | They propose a class-wise regularization method that preserves eigenvectors for each class shape . they propose spectral regularization to preserve eenvector shape after learning new tasks . |
| Outcome: | The proposed method improves performance on two benchmark datasets. |
Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection (2023.findings-acl)
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| Challenge: | Existing approaches to cross-lingual transfer learning for event detection are mixed with event-discriminative context. |
| Approach: | They propose a method where representations are augmented with additional context to bridge the gap between languages while enriching contextual information to facilitate ED. |
| Outcome: | The proposed model performs well on three languages. |