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Qatar / Education

Carnegie Mellon Qatar professor key organiser for EMNLP2023 conference

Published: 23 Jan 2024 - 09:42 am | Last Updated: 23 Jan 2024 - 09:42 am

The Peninsula

Doha, Qatar: Houda Bouamor (pictured) served as a key organiser for the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP2023), an event that drew about 4,000 global experts in the area of Natural Language Processing (NLP), a subfield of artificial intelligence.  Bouamor is an associate teaching professor of information systems at Carnegie Mellon University in Qatar, and an expert in NLP.

This year, the conference took place in Singapore. Bouamor was one of the programme chairs for the conference, and Kemal Oflazer, a CMU-Q teaching professor of computer science and an expert in Turkish NLP, served as an ethics chair.

“There were so many interesting researchers and practitioners at the conference that provided valuable insight into the possibilities, and the challenges of processing human languages. This was particularly pertinent given the surge of Generative AI, a crucial task within our field,” said Bouamor. “The consistent theme throughout was that NLP should be human-centred. Researchers are looking at novel ways that generative AI and NLP can improve people’s lives, like detecting mental health issues, providing accessibility for the hearing-impaired, and strengthening cultural identity in less represented languages.”

Michael Trick, Dean of CMU-Q, is a strong proponent of thoughtful, human-centred AI. “At Carnegie Mellon, we believe that teaching our students responsible AI is the key to positive, ethical progress. Of course, the theory and technical intricacies of artificial intelligence are important, but equally important is to think through the impact of innovation.”  Bouamor helped ensure the conference addressed the challenges in the field including the importance of large language models in building Generative AI tools.

“We felt it was imperative to discuss issues like sustainability and bias that are inherent in large language modeling. We also want to consider how to preserve the culture and identity that is deeply embedded in language. Everyone in this field should be reflecting on how to make advances that will have a positive impact on the world.”