132 citations · 214 across the 10 of their papers we have counts for
10 papers
MultiSChuBERT: Effective Multimodal Fusion for Scholarly Document Quality Prediction
Gideon Maillette de Buy Wenniger, Thomas van Dongen, Lambert Schomaker
Automatic assessment of the quality of scholarly documents is a difficult task with high potential impact. Multimodality, in particular the addition of visual information next to t…
Active learning for reducing labeling effort in text classification tasks
Pieter Floris Jacobs, Gideon Maillette de Buy Wenniger, Marco Wiering +1
Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Ac…
SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction
Thomas van Dongen, Gideon Maillette de Buy Wenniger, Lambert Schomaker
Predicting the number of citations of scholarly documents is an upcoming task in scholarly document processing. Besides the intrinsic merit of this information, it also has a wider…
Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction
Gideon Maillette de Buy Wenniger, Thomas van Dongen, Eleri Aedmaa +3
Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in sol…
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…
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…
Data Selection with Feature Decay Algorithms Using an Approximated Target Side
Alberto Poncelas, Gideon Maillette de Buy Wenniger, Andy Way
Data selection techniques applied to neural machine translation (NMT) aim to increase the performance of a model by retrieving a subset of sentences for use as training data. One o…
Investigating Backtranslation in Neural Machine Translation
Alberto Poncelas, Dimitar Shterionov, Andy Way +2
A prerequisite for training corpus-based machine translation (MT) systems -- either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of high-quality parallel data. Th…