Natural Language Processing

Leveraging Readily Available Data for Low Resource Natural Language Processing

Derry Wijaya, Assistant Professor, Computer Science, Boston University

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KAUST

State-of-the-art Natural Language Processing (NLP) systems nowadays are dominated by machine learning and deep learning models. However, most of these models often only work well when there are abundant labeled data for training. Furthermore, as these models typically have a large number of parameters, they require large compute resources to train. For the majority of languages in the world and the researchers working on these languages, however, abundant labeled data are a privilege and so do compute resources. How can we train generalizable NLP models that are effective even when labeled data are scarce and compute resources are limited? In this talk, I will present some of our solutions that leverage unsupervised or few-shot learning and readily available multilingual resources or multimodal data to improve machine translation and nuanced text classification such as news framing under these low resource settings.

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

The Wild West of NLP Modeling, Evaluation, and Documentation

Dr. Nazneen Rajani, Research Lead, Hugging Face, California

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B2 L5 R5220

Are commercial APIs from Microsoft, Google, and Amazon for NLP tasks any better than rule-based systems? Is documentation for LLMs accessible to non-expert users in the industry? Our work on creating a unified toolkit for evaluation (robustness gym) and reporting (interactive model cards) attempts to address these questions.

Navigating NLP for Underrepresented Languages: Dataset Challenges, Efficient Techniques, and Evaluations

Fajri Koto, Postdoc, MBZUAI

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B9 L4 R4225

Democratizing NLP across numerous languages is a non-trivial task, as it may encounter challenges related to data scarcity, limitations in computational resources, and the intricacies of multilingual and multicultural diversity. The speaker will discuss the efforts and findings in tackling these challenges in this talk. To begin, data scarcity and inconsistency in metadata present common obstacles in low-resource NLP, complicating the understanding of the NLP landscape for low-resource languages.

KAUST Prospective student: Fernando Patricio Zhapa Camacho

1 min read ·
Fernando is a 23-year-old Information Technology graduate fromYachay Tech University in Urcuquí, Ecuador. He has planned to continue his academic career at KAUST under the supervision of Professor Robert Hoehndorf.

Meet KAUST Prospective Student: Asaad AlGhamdi

1 min read ·
Asaad AlGhamdi is a 24-year-old graduate who will join KAUST in the fall from King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. Asaad will join the University as a M.S. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

KAUST Prospective Student: Damilya Saduakhas

1 min read ·
Damilya Saduakhas, 21 years old, from Kazakhstan, obtained a B.Sc. degree in mathematics from the Nazarbayev University, Kazakhstan. Damilya has been selected as a young scientist to participate in the summer Data Lab from Yessenov Foundation and will join KAUST in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics Research group under the supervision of Professor David Bolin.

Meet KAUST new student: Xiuying Chen

1 min read ·
Xiuying Chen is a computer science graduate who joined KAUST from Wuhan University, China. Chen believes that KAUST provides an excellent research environment to carry out scientific research.

Xiuying Chen

Ph.D. Student, Computer Science
Xiuying Chen received her B.S. degree in Computer Science from Wuhan University in 2018, followed by a Master's degree from Wangxuan Institute of Computer Technology at Peking University. She is currently a Ph.D. student at King Abdullah University of Science and Technology (KAUST), focusing on improving accuracy, robustness, and trustworthiness of Text Generation. She is also interested in interdisciplinary research, such as in applying large language models within healthcare and scientific discovery. She has made significant contributions with over 40 papers published in top-tier conferences

Robert Werfelmann

Robert Werfelmann obtained a Master's Degree in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). He did his thesis in a task of Natural Language Processing where the goal is to predict the native language of the author of a non-native text. During his studies, he has built and improved skills in several topics including algorithm design and analysis, concurrent computing, computer networking, and artificial intelligence fields such as