166 citations · 607 across the 32 of their papers we have counts for
35 papers
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…
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…
Adaptive Machine Translation with Large Language Models
Yasmin Moslem, Rejwanul Haque, John D. Kelleher +1
Consistency is a key requirement of high-quality translation. It is especially important to adhere to pre-approved terminology and adapt to corrected translations in domain-specifi…
Translation Word-Level Auto-Completion: What can we achieve out of the box?
Yasmin Moslem, Rejwanul Haque, Andy Way
Research on Machine Translation (MT) has achieved important breakthroughs in several areas. While there is much more to be done in order to build on this success, we believe that t…
Domain-Specific Text Generation for Machine Translation
Yasmin Moslem, Rejwanul Haque, John D. Kelleher +1
Preservation of domain knowledge from the source to target is crucial in any translation workflow. It is common in the translation industry to receive highly specialized projects,…
Dependency Graph-to-String Statistical Machine Translation
Liangyou Li, Andy Way, Qun Liu
We present graph-based translation models which translate source graphs into target strings. Source graphs are constructed from dependency trees with extra links so that non-syntac…
Using Multiple Subwords to Improve English-Esperanto Automated Literary Translation Quality
Alberto Poncelas, Jan Buts, James Hadley +1
Building Machine Translation (MT) systems for low-resource languages remains challenging. For many language pairs, parallel data are not widely available, and in such cases MT mode…
The Impact of Indirect Machine Translation on Sentiment Classification
Alberto Poncelas, Pintu Lohar, Andy Way +1
Sentiment classification has been crucial for many natural language processing (NLP) applications, such as the analysis of movie reviews, tweets, or customer feedback. A sufficient…
Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation
Xabier Soto, Dimitar Shterionov, Alberto Poncelas +1
Machine translation (MT) has benefited from using synthetic training data originating from translating monolingual corpora, a technique known as backtranslation. Combining backtran…
Facilitating Access to Multilingual COVID-19 Information via Neural Machine Translation
Andy Way, Rejwanul Haque, Guodong Xie +3
Every day, more people are becoming infected and dying from exposure to COVID-19. Some countries in Europe like Spain, France, the UK and Italy have suffered particularly badly fro…
Multiple Segmentations of Thai Sentences for Neural Machine Translation
Alberto Poncelas, Wichaya Pidchamook, Chao-Hong Liu +2
Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a h…
A Tool for Facilitating OCR Postediting in Historical Documents
Alberto Poncelas, Mohammad Aboomar, Jan Buts +2
Optical character recognition (OCR) for historical documents is a complex procedure subject to a unique set of material issues, including inconsistencies in typefaces and low quali…
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Georg Rehm, Katrin Marheinecke, Stefanie Hegele +44
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality. However, language barriers impacting business, cr…
Selecting Artificially-Generated Sentences for Fine-Tuning Neural Machine Translation
Alberto Poncelas, Andy Way
Neural Machine Translation (NMT) models tend to achieve best performance when larger sets of parallel sentences are provided for training. For this reason, augmenting the training…
Getting Gender Right in Neural Machine Translation
Eva Vanmassenhove, Christian Hardmeier, Andy Way
Speakers of different languages must attend to and encode strikingly different aspects of the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such d…
Combining SMT and NMT Back-Translated Data for Efficient NMT
Alberto Poncelas, Maja Popovic, Dimitar Shterionov +2
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training…
Transductive Data-Selection Algorithms for Fine-Tuning Neural Machine Translation
Alberto Poncelas, Gideon Maillette de Buy Wenniger, Andy Way
Machine Translation models are trained to translate a variety of documents from one language into another. However, models specifically trained for a particular characteristics of…
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation
Eva Vanmassenhove, Dimitar Shterionov, Andy Way
This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show ho…
Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods
Alberto Poncelas, Gideon Maillette de Buy Wenniger, Andy Way
Data selection has proven its merit for improving Neural Machine Translation (NMT), when applied to authentic data. But the benefit of using synthetic data in NMT training, produce…
No Padding Please: Efficient Neural Handwriting Recognition
Gideon Maillette de Buy Wenniger, Lambert Schomaker, Andy Way
Neural handwriting recognition (NHR) is the recognition of handwritten text with deep learning models, such as multi-dimensional long short-term memory (MDLSTM) recurrent neural ne…
ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction
Eva Vanmassenhove, Amit Moryossef, Alberto Poncelas +2
We present our system for the CLIN29 shared task on cross-genre gender detection for Dutch. We experimented with a multitude of neural models (CNN, RNN, LSTM, etc.), more "traditio…
The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task
Alberto Poncelas, Andy Way, Kepa Sarasola
In this paper we present the ADAPT system built for the Basque to English Low Resource MT Evaluation Campaign. Basque is a low-resourced, morphologically-rich language. This poses…