Zobrazeno 1 - 10
of 10 511
pro vyhledávání: '"Van Nguyen, At"'
The rapid spread of information in the digital age highlights the critical need for effective fact-checking tools, particularly for languages with limited resources, such as Vietnamese. In response to this challenge, we introduce ViFactCheck, the fir
Externí odkaz:
http://arxiv.org/abs/2412.15308
In this paper, we aimed to develop a neural parser for Vietnamese based on simplified Head-Driven Phrase Structure Grammar (HPSG). The existing corpora, VietTreebank and VnDT, had around 15% of constituency and dependency tree pairs that did not adhe
Externí odkaz:
http://arxiv.org/abs/2411.17270
Natural Language Inference (NLI) is a task within Natural Language Processing (NLP) that holds value for various AI applications. However, there have been limited studies on Natural Language Inference in Vietnamese that explore the concept of joint m
Externí odkaz:
http://arxiv.org/abs/2411.13407
Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) in knowledge-intensive tasks such as those from medical domain. However, the sensitive nature of the medical domain ne
Externí odkaz:
http://arxiv.org/abs/2411.09213
In this paper we investigate the existence, uniqueness and exponential stability of pseudo almost periodic (PAP-) mild solutions of the parabolic-elliptic (P-E) Keller-Segel system on a bounded domain $\Omega\in \mathbb{R}^n$ with smooth boundary. Fi
Externí odkaz:
http://arxiv.org/abs/2411.06341
Autor:
To, Long Truong, Le, Hung Tuan, Nguyen, Dat Van-Thanh, Nguyen, Manh Trong, Nguyen, Tri Thien, Van Huynh, Tin, Van Nguyen, Kiet
Large Language Models (LLMs), with gradually improving reading comprehension and reasoning capabilities, are being applied to a range of complex language tasks, including the automatic generation of language data for various purposes. However, resear
Externí odkaz:
http://arxiv.org/abs/2411.05641
Autor:
Van Nguyen, Chien, Shen, Xuan, Aponte, Ryan, Xia, Yu, Basu, Samyadeep, Hu, Zhengmian, Chen, Jian, Parmar, Mihir, Kunapuli, Sasidhar, Barrow, Joe, Wu, Junda, Singh, Ashish, Wang, Yu, Gu, Jiuxiang, Dernoncourt, Franck, Ahmed, Nesreen K., Lipka, Nedim, Zhang, Ruiyi, Chen, Xiang, Yu, Tong, Kim, Sungchul, Deilamsalehy, Hanieh, Park, Namyong, Rimer, Mike, Zhang, Zhehao, Yang, Huanrui, Rossi, Ryan A., Nguyen, Thien Huu
Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, making them ideal for various settings including on-device, mobile, edge d
Externí odkaz:
http://arxiv.org/abs/2410.20011
Autor:
Van Nguyen, Chien, Nguyen, Huy Huu, Pham, Thang M., Zhang, Ruiyi, Deilamsalehy, Hanieh, Mathur, Puneet, Rossi, Ryan A., Bui, Trung, Lai, Viet Dac, Dernoncourt, Franck, Nguyen, Thien Huu
Efficient long-context language modeling remains a significant challenge in Natural Language Processing (NLP). While Transformers dominate language tasks, they struggle with long sequences due to quadratic computational complexity in training and lin
Externí odkaz:
http://arxiv.org/abs/2410.18572
Vietnamese, a low-resource language, is typically categorized into three primary dialect groups that belong to Northern, Central, and Southern Vietnam. However, each province within these regions exhibits its own distinct pronunciation variations. De
Externí odkaz:
http://arxiv.org/abs/2410.03458
This study introduces an innovative automatic labeling framework to address the challenges of lexical normalization in social media texts for low-resource languages like Vietnamese. Social media data is rich and diverse, but the evolving and varied l
Externí odkaz:
http://arxiv.org/abs/2409.20467