Zobrazeno 1 - 10
of 21 735
pro vyhledávání: '"Lankford, A"'
Autor:
Lankford, Séamus, Way, Andy
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 languages. This study presents a comprehensive exploration of leveraging La
Externí odkaz:
http://arxiv.org/abs/2410.23890
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 with the nuances and style required for organisation-specific transla
Externí odkaz:
http://arxiv.org/abs/2409.03454
Autor:
Lankford, Séamus, Grimes, Diarmuid
Publikováno v:
Proceedings of The 28th Irish Conference on Artificial Intelligence and Cognitive Science. 2771. CEUR-WS, 2020
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 and what values to assign to parameters. In most cases, default hyperpa
Externí odkaz:
http://arxiv.org/abs/2403.03781
Publikováno v:
In Proceedings of the 1st Workshop on Open Community-Driven Machine Translation, pages 15-20, Tampere, Finland. European Association for Machine Translation, 2023
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 built upon the widely-adopted OpenNMT ecosystem, and is particularly
Externí odkaz:
http://arxiv.org/abs/2403.03582
Publikováno v:
In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6753-6758, Marseille, France. European Language Resources Association, 2022
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 available for developing translation models. Furthermore, the development
Externí odkaz:
http://arxiv.org/abs/2403.03575
Publikováno v:
Information 2023, 14(12), 638
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 this technology, its impact on developing high-quality Machine Transla
Externí odkaz:
http://arxiv.org/abs/2403.02370
Publikováno v:
Language Resources and Evaluation 57, 1671-1696, (2023)
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 both technical and non-technical users who work in the field of machine
Externí odkaz:
http://arxiv.org/abs/2403.02367
Publikováno v:
Information 2022, 13(7), 309
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 English--Irish pair. SentencePiece models using both Byte Pair Encoding (BP
Externí odkaz:
http://arxiv.org/abs/2403.02366
Publikováno v:
Proceedings of Machine Translation Summit XVIII: Research Track 2021
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 training data. As a consequence, relatively few experiments have been carried
Externí odkaz:
http://arxiv.org/abs/2403.01985
Autor:
Lankford, Séamus
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 into its efficacy for low-resource language pairs including both the En
Externí odkaz:
http://arxiv.org/abs/2403.01580