Zobrazeno 1 - 10
of 252
pro vyhledávání: '"A. Afli"'
Autor:
Zebrowski, Adam, Afli, Haithem
Country instability is a global issue, with unpredictably high levels of instability thwarting socio-economic growth and possibly causing a slew of negative consequences. As a result, uncertainty prediction models for a country are becoming increasin
Externí odkaz:
http://arxiv.org/abs/2411.06639
Autor:
Frank, Manuel, Afli, Haithem
We propose an approach to enhance sentence embeddings by applying generative text models for data augmentation at inference time. Unlike conventional data augmentation that utilises synthetic training data, our approach does not require access to mod
Externí odkaz:
http://arxiv.org/abs/2411.04914
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
Publikováno v:
Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)
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 Covid-adapted generic 55k corpus from the Directorate General of Transla
Externí odkaz:
http://arxiv.org/abs/2403.01196
Distributed collaborative machine learning (DCML) is a promising method in the Internet of Things (IoT) domain for training deep learning models, as data is distributed across multiple devices. A key advantage of this approach is that it improves dat
Externí odkaz:
http://arxiv.org/abs/2307.13266