34 citations · 95 across the 11 of their papers we have counts for
12 papers
Sociotechnical Effects of Machine Translation
Joss Moorkens, Andy Way, Séamus Lankford
While the previous chapters have shown how machine translation (MT) can be useful, in this chapter we discuss some of the side-effects and risks that are associated, and how they m…
Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages
Séamus Lankford, Andy Way
In an evolving landscape of crisis communication, the need for robust and adaptable Machine Translation (MT) systems is more pressing than ever, particularly for low-resource langu…
How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes
Inacio Vieira, Will Allred, Séamus Lankford +2
Decoder-only LLMs have shown impressive performance in MT due to their ability to learn from extensive datasets and generate high-quality translations. However, LLMs often struggle…
Neural Architecture Search using Particle Swarm and Ant Colony Optimization
Séamus Lankford, Diarmuid Grimes
Neural network models have a number of hyperparameters that must be chosen along with their architecture. This can be a heavy burden on a novice user, choosing which architecture a…
Design of an Open-Source Architecture for Neural Machine Translation
Séamus Lankford, Haithem Afli, Andy Way
adaptNMT is an open-source application that offers a streamlined approach to the development and deployment of Recurrent Neural Networks and Transformer models. This application is…
gaHealth: An English-Irish Bilingual Corpus of Health Data
Séamus Lankford, Haithem Afli, Órla Ní Loinsigh +1
Machine Translation is a mature technology for many high-resource language pairs. However in the context of low-resource languages, there is a paucity of parallel data datasets ava…
adaptMLLM: Fine-Tuning Multilingual Language Models on Low-Resource Languages with Integrated LLM Playgrounds
Séamus Lankford, Haithem Afli, Andy Way
The advent of Multilingual Language Models (MLLMs) and Large Language Models has spawned innovation in many areas of natural language processing. Despite the exciting potential of…
adaptNMT: an open-source, language-agnostic development environment for Neural Machine Translation
Séamus Lankford, Haithem Afli, Andy Way
adaptNMT streamlines all processes involved in the development and deployment of RNN and Transformer neural translation models. As an open-source application, it is designed for bo…
Human Evaluation of English--Irish Transformer-Based NMT
Séamus Lankford, Haithem Afli, Andy Way
In this study, a human evaluation is carried out on how hyperparameter settings impact the quality of Transformer-based Neural Machine Translation (NMT) for the low-resourced Engli…
Transformers for Low-Resource Languages: Is Féidir Linn!
Séamus Lankford, Haithem Afli, Andy Way
The Transformer model is the state-of-the-art in Machine Translation. However, in general, neural translation models often under perform on language pairs with insufficient trainin…
Enhancing Neural Machine Translation of Low-Resource Languages: Corpus Development, Human Evaluation and Explainable AI Architectures
Séamus Lankford
In the current machine translation (MT) landscape, the Transformer architecture stands out as the gold standard, especially for high-resource language pairs. This research delves i…
Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021
Séamus Lankford, Haithem Afli, Andy Way
Translation models for the specific domain of translating Covid data from English to Irish were developed for the LoResMT 2021 shared task. Domain adaptation techniques, using a Co…