1 code implementation • EMNLP (ACL) 2021 • Raymond Li, Wen Xiao, Lanjun Wang, Hyeju Jang, Giuseppe Carenini
Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging.
no code implementations • NAACL (DeeLIO) 2021 • Hyeju Jang, Seojin Bang, Wen Xiao, Giuseppe Carenini, Raymond Ng, Young ji Lee
Text classification has wide-ranging applications in various domains.
no code implementations • 10 Apr 2024 • Rahul Mehta, Andrew Hoblitzell, Jack O'Keefe, Hyeju Jang, Vasudeva Varma
Hallucinations in large language models (LLMs) have recently become a significant problem.
1 code implementation • 22 Jun 2023 • Ratanond Koonchanok, Yanling Pan, Hyeju Jang
For instance, Cybersecurity is the most discussed topic among those with occupations related to computer and math, and Education is the most discussed topic among those in academic and research.
1 code implementation • 31 Aug 2021 • Raymond Li, Wen Xiao, Lanjun Wang, Hyeju Jang, Giuseppe Carenini
Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging.
no code implementations • EMNLP (NLP-COVID19) 2020 • Hyeju Jang, Emily Rempel, Giuseppe Carenini, Naveed Janjua
We also examine people's sentiment about COVID-19 related issues.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)
no code implementations • LREC 2020 • Nadiya Straton, Hyeju Jang, Raymond Ng
Success of the rigorous annotation process on identifying stigma is reconfirmed by achieving high prediction rate with CNN.
no code implementations • EMNLP 2017 • Yohan Jo, Michael Yoder, Hyeju Jang, Carolyn Ros{\'e}
We present an unsupervised model of dialogue act sequences in conversation.
no code implementations • WS 2017 • Hyeju Jang, Keith Maki, Eduard Hovy, Carolyn Ros{\'e}
In this paper, we present a novel and highly effective method for induction and application of metaphor frame templates as a step toward detecting metaphor in extended discourse.