6 citations · 7 across the 9 of their papers we have counts for
7 papers · 1 filter
VLURes: Benchmarking VLM Visual and Linguistic Understanding in Low-Resource Languages
Jesse Atuhurra, Iqra Ali, Tomoya Iwakura +2
Vision Language Models (VLMs) are pivotal for advancing perception in intelligent agents. Yet, evaluation of VLMs remains limited to predominantly English-centric benchmarks in whi…
NERsocial: Efficient Named Entity Recognition Dataset Construction for Human-Robot Interaction Utilizing RapidNER
Jesse Atuhurra, Hidetaka Kamigaito, Hiroki Ouchi +2
Adapting named entity recognition (NER) methods to new domains poses significant challenges. We introduce RapidNER, a framework designed for the rapid deployment of NER systems thr…
Constructing Multilingual Visual-Text Datasets Revealing Visual Multilingual Ability of Vision Language Models
Jesse Atuhurra, Iqra Ali, Tatsuya Hiraoka +3
Large language models (LLMs) have increased interest in vision language models (VLMs), which process image-text pairs as input. Studies investigating the visual understanding abili…
Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili
Jesse Atuhurra, Hiroyuki Shindo, Hidetaka Kamigaito +1
Many attempts have been made in multilingual NLP to ensure that pre-trained language models, such as mBERT or GPT2 get better and become applicable to low-resource languages. To ac…
Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferences
Jesse Atuhurra, Hidetaka Kamigaito
Natural language processing (NLP) has grown significantly since the advent of the Transformer architecture. Transformers have given birth to pre-trained large language models (PLMs…
Domain Adaptation in Intent Classification Systems: A Review
Jesse Atuhurra, Hidetaka Kamigaito, Taro Watanabe +1
Dialogue agents, which perform specific tasks, are part of the long-term goal of NLP researchers to build intelligent agents that communicate with humans in natural language. Such…
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models
Jesse Atuhurra, Seiveright Cargill Dujohn, Hidetaka Kamigaito +2
Natural language processing (NLP) practitioners are leveraging large language models (LLM) to create structured datasets from semi-structured and unstructured data sources such as…