activity
20162024
most citedA Neural Knowledge Language Model

112 citations · 124 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Advancing AI with Integrity: Ethical Challenges and Solutions in Neural Machine Translation

Richard Kimera, Yun-Seon Kim, Heeyoul Choi

This paper addresses the ethical challenges of Artificial Intelligence in Neural Machine Translation (NMT) systems, emphasizing the imperative for developers to ensure fairness and…

cs.CL20243 cited

Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda

Richard Kimera, Daniela N. Rim, Joseph Kirabira +2

Depression is a global burden and one of the most challenging mental health conditions to control. Experts can detect its severity early using the Beck Depression Inventory (BDI) q…

cs.CL2023

Fast Training of NMT Model with Data Sorting

Daniela N. Rim, Kimera Richard, Heeyoul Choi

The Transformer model has revolutionized Natural Language Processing tasks such as Neural Machine Translation, and many efforts have been made to study the Transformer architecture…

cs.NI2023

Advanced Scaling Methods for VNF deployment with Reinforcement Learning

Namjin Seo, DongNyeong Heo, Heeyoul Choi

Network function virtualization (NFV) and software-defined network (SDN) have become emerging network paradigms, allowing virtualized network function (VNF) deployment at a low cos…

cs.CL20239 cited

Building a Parallel Corpus and Training Translation Models Between Luganda and English

Richard Kimera, Daniela N. Rim, Heeyoul Choi

Neural machine translation (NMT) has achieved great successes with large datasets, so NMT is more premised on high-resource languages. This continuously underpins the low resource…

cs.CL2016112 cited

A Neural Knowledge Language Model

Sungjin Ahn, Heeyoul Choi, Tanel Pärnamaa +1

Current language models have a significant limitation in the ability to encode and decode factual knowledge. This is mainly because they acquire such knowledge from statistical co-…