Results 61 to 70 of about 221,770 (298)
Neural Network Machine Translation Method Based on Unsupervised Domain Adaptation
Complexity, 2020 Relying on large-scale parallel corpora, neural machine translation has achieved great success in certain language pairs. However, the acquisition of high-quality parallel corpus is one of the main difficulties in machine translation research.
Rui Wang
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Applied Sciences, 2023 Neural machine translation has achieved good translation results, but needs further improvement in low-resource and domain-specific translation. To this end, the paper proposed to incorporate source language syntactic information into neural machine ...
Yanna Sang, Yuan Chen, Juwei Zhang
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A Stochastic Decoder for Neural Machine Translation [PDF]
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018 The process of translation is ambiguous, in that there are typically many valid trans- lations for a given sentence. This gives rise to significant variation in parallel cor- pora, however, most current models of machine translation do not account for this variation, instead treating the prob- lem as a deterministic process.
Schulz, P., Aziz, W., Cohn, T.
openaire +5 more sources
From omics to AI—mapping the pathogenic pathways in type 2 diabetes
FEBS Letters, EarlyView.Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan +2 more
wiley +1 more source
A Challenge Set Approach to Evaluating Machine Translation
, 2017 Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain?
Cherry, Colin +2 more
core +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
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Improving Transformer-Based Neural Machine Translation with Prior Alignments
Complexity, 2021 Transformer is a neural machine translation model which revolutionizes machine translation. Compared with traditional statistical machine translation models and other neural machine translation models, the recently proposed transformer model radically ...
Thien Nguyen +3 more
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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
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Domain Control for Neural Machine Translation
, 2017 Machine translation systems are very sensitive to the domains they were trained on. Several domain adaptation techniques have been deeply studied. We propose a new technique for neural machine translation (NMT) that we call domain control which is ...
Crego, Josep +2 more
core +1 more source
Molecular Oncology, EarlyView.Dual targeting of AKT and mTOR using MK2206 and RAD001 reduces tumor burden in an intracardiac colon cancer circulating tumor cell xenotransplantation model. Analysis of AKT isoform‐specific knockdowns in CTC‐MCC‐41 reveals differentially regulated proteins and phospho‐proteins by liquid chromatography coupled mass spectrometry. Circulating tumor cells
Daniel J. Smit +19 more
wiley +1 more source