Zobrazeno 1 - 10
of 82
pro vyhledávání: '"ELDARDIRY, HODA"'
Existing text simplification or paraphrase datasets mainly focus on sentence-level text generation in a general domain. These datasets are typically developed without using domain knowledge. In this paper, we release a novel dataset, VTechAGP, which
Externí odkaz:
http://arxiv.org/abs/2411.04825
Many real-world problems, such as controlling swarms of drones and urban traffic, naturally lend themselves to modeling as multi-agent reinforcement learning (RL) problems. However, existing multi-agent RL methods often suffer from scalability challe
Externí odkaz:
http://arxiv.org/abs/2410.02516
Autor:
Gong, Jiaying, Eldardiry, Hoda
Publikováno v:
LREC-COLING 2024
The goal of few-shot relation extraction is to predict relations between name entities in a sentence when only a few labeled instances are available for training. Existing few-shot relation extraction methods focus on uni-modal information such as te
Externí odkaz:
http://arxiv.org/abs/2403.00724
Autor:
Gong, Jiaying, Eldardiry, Hoda
E-commerce platforms should provide detailed product descriptions (attribute values) for effective product search and recommendation. However, attribute value information is typically not available for new products. To predict unseen attribute values
Externí odkaz:
http://arxiv.org/abs/2402.08802
Autor:
Yuan, Chenhan, Eldardiry, Hoda
Temporal knowledge graphs (TKGs) have shown promise for reasoning tasks by incorporating a temporal dimension to represent how facts evolve over time. However, existing TKG reasoning (TKGR) models lack explainability due to their black-box nature. Re
Externí odkaz:
http://arxiv.org/abs/2310.04889
Federated Learning (FL) plays a critical role in distributed systems. In these systems, data privacy and confidentiality hold paramount importance, particularly within edge-based data processing systems such as IoT devices deployed in smart homes. FL
Externí odkaz:
http://arxiv.org/abs/2308.13662
Existing attribute-value extraction (AVE) models require large quantities of labeled data for training. However, new products with new attribute-value pairs enter the market every day in real-world e-Commerce. Thus, we formulate AVE in multi-label fe
Externí odkaz:
http://arxiv.org/abs/2308.08413
We examine the problem of two-point boundary optimal control of nonlinear systems over finite-horizon time periods with unknown model dynamics by employing reinforcement learning. We use techniques from singular perturbation theory to decompose the c
Externí odkaz:
http://arxiv.org/abs/2306.05482
One of the challenges in contrastive learning is the selection of appropriate \textit{hard negative} examples, in the absence of label information. Random sampling or importance sampling methods based on feature similarity often lead to sub-optimal p
Externí odkaz:
http://arxiv.org/abs/2206.01197
Autor:
Gong, Jiaying, Eldardiry, Hoda
Publikováno v:
LREC-COLING 2024
In relation triplet extraction (RTE), recognizing unseen relations for which there are no training instances is a challenging task. Efforts have been made to recognize unseen relations based on question-answering models or relation descriptions. Howe
Externí odkaz:
http://arxiv.org/abs/2112.04539