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Found 16 papers, 1 papers with code
SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction.
We also show the merit of using more training data and longer input for number of citations prediction.
MultiSChuBERT: Effective Multimodal Fusion for Scholarly Document Quality Prediction
Using BERT$_{\textrm{BASE}}$ embeddings, on the (log) number of citations prediction task with the ACL-BiblioMetry dataset, our MultiSChuBERT (text+visual) model obtains an $R^{2}$ score of 0. 454 compared to 0. 432 for the SChuBERT (text only) model.
Active learning for reducing labeling effort in text classification tasks
Furthermore, we explore the influence of the query-pool size on the performance of AL.
SChuBERT: Scholarly Document Chunks with BERT-encoding boost Citation Count Prediction
We also show the merit of using more training data and longer input for number of citations prediction.
Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction
To tackle these problems, we propose the use of HANs combined with structure-tags which mark the role of sentences in the document.
Combining SMT and NMT Back-Translated Data for Efficient NMT
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training.
Combining PBSMT and NMT Back-translated Data for Efficient NMT
Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training.
Transductive Data-Selection Algorithms for Fine-Tuning Neural Machine Translation
Machine Translation models are trained to translate a variety of documents from one language into another.
Adaptation of Machine Translation Models with Back-translated Data using Transductive Data Selection Methods
These methods ensure that selected sentences share n-grams with the test set so the NMT model can be adapted to translate it.
No Padding Please: Efficient Neural Handwriting Recognition
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 networks.
Ranked #15 on
Handwritten Text Recognition
on IAM
Data Selection with Feature Decay Algorithms Using an Approximated Target Side
A limitation of these methods to date is that using the source-side test set does not by itself guarantee that sentences are selected with correct translations, or translations that are suitable given the test-set domain.
Investigating Backtranslation in Neural Machine Translation
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.
