Results 81 to 90 of about 53,972 (305)
Discover ComputingThe decoding phase is a crucial component in machine translation systems, alongside the creation of the model. Beam search is the most commonly used algorithm for decoding in these systems.
Emre Satir, Hasan Bulut
doaj +1 more source
Dynamic adaptation of neural machine-translation systems through translation exemplars
, 2022 This project aims to study the impact of adapting neural machine translation (NMT) systems through translation exemplars, determine the optimal similarity metric(s) for retrieving informative exemplars, and, verify the usefulness of this approach for ...
Tezcan, Arda
core
Substructure-based Neural Machine Translation for Retrosynthetic Prediction
, 2020 This work presents a new template-free neural machine translation method for retrosynthetic reaction prediction by learning the chemical change at a substructural level.
Taek, Kang +3 more
core +1 more source
Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment
Molecular Oncology, EarlyView.This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley +1 more source
Semantic Neural Machine Translation Using AMR
Transactions of the Association for Computational Linguistics, 2019 It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models.
Song, Linfeng +4 more
doaj +1 more source
Residual Information Flow for Neural Machine Translation
IEEE Access, 2022 Automatic machine translation plays an important role in reducing language barriers between people speaking different languages. Deep neural networks (DNN) have attained major success in diverse research fields such as computer vision, information ...
Shereen A. Mohamed +2 more
doaj +1 more source
Deep Neural Machine Translation with Weakly-Recurrent Units [PDF]
, 2018 Recurrent neural networks (RNNs) have represented for years the state of the art in neural machine translation. Recently, new architectures have been proposed, which can leverage parallel computation on GPUs better than classical RNNs.
Federico, Marcello +3 more
core
Molecular Oncology, EarlyView.Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim +3 more
wiley +1 more source
Byte-based Neural Machine Translation [PDF]
Proceedings of the First Workshop on Subword and Character Level Models in NLP, 2017 This paper presents experiments compar- ing character-based and byte-based neural machine translation systems. The main motivation of the byte-based neural ma- chine translation system is to build multi- lingual neural machine translation systems that can share the same vocabulary.
Ruiz Costa-Jussà, Marta +2 more
openaire +2 more sources
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source