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pro vyhledávání: '"Lee, Jean"'
Visually Rich Documents (VRDs) are essential in academia, finance, medical fields, and marketing due to their multimodal information content. Traditional methods for extracting information from VRDs depend on expert knowledge and manual labor, making
Externí odkaz:
http://arxiv.org/abs/2408.01287
Autor:
Gechter, Michael, Hirano, Keisuke, Lee, Jean, Mahmud, Mahreen, Mondal, Orville, Morduch, Jonathan, Ravindran, Saravana, Shonchoy, Abu S.
Policy decisions often depend on evidence generated elsewhere. We take a Bayesian decision-theoretic approach to choosing where to experiment to optimize external validity. We frame external validity through a policy lens, developing a prior specific
Externí odkaz:
http://arxiv.org/abs/2405.13241
Autor:
Ding, Yihao, Vaiani, Lorenzo, Han, Caren, Lee, Jean, Garza, Paolo, Poon, Josiah, Cagliero, Luca
This paper presents a groundbreaking multimodal, multi-task, multi-teacher joint-grained knowledge distillation model for visually-rich form document understanding. The model is designed to leverage insights from both fine-grained and coarse-grained
Externí odkaz:
http://arxiv.org/abs/2402.17983
Large Language Models (LLMs) have shown remarkable capabilities across a wide variety of Natural Language Processing (NLP) tasks and have attracted attention from multiple domains, including financial services. Despite the extensive research into gen
Externí odkaz:
http://arxiv.org/abs/2402.02315
There has been growing interest in applying NLP techniques in the financial domain, however, resources are extremely limited. This paper introduces StockEmotions, a new dataset for detecting emotions in the stock market that consists of 10,000 Englis
Externí odkaz:
http://arxiv.org/abs/2301.09279
Autor:
Lee, Jean, Lim, Taejun, Lee, Heejun, Jo, Bogeun, Kim, Yangsok, Yoon, Heegeun, Han, Soyeon Caren
Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effecti
Externí odkaz:
http://arxiv.org/abs/2208.10684