Jan Niehues

Jan Niehues
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Jan Niehues
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Computer Science - Computation and Language (3)
 
High Energy Physics - Phenomenology (2)
 
Computer Science - Neural and Evolutionary Computing (1)
 
Computer Science - Learning (1)

Publications Authored By Jan Niehues

Hadronic jets in deeply inelastic electron-proton collisions are produced by the scattering of a parton from the proton with the virtual gauge boson mediating the interaction. The HERA experiments have performed precision measurements of inclusive single jet production and di-jet production in the Breit frame, which provide important constraints on the strong coupling constant and on parton distributions in the proton. We describe the calculation of the next-to-next-to-leading order (NNLO) QCD corrections to these processes, and assess their size and impact. Read More

In this paper, we present our first attempts in building a multilingual Neural Machine Translation framework under a unified approach. We are then able to employ attention-based NMT for many-to-many multilingual translation tasks. Our approach does not require any special treatment on the network architecture and it allows us to learn minimal number of free parameters in a standard way of training. Read More

Recently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical machine translation (SMT)-based systems, in some cases, the NMT system produces translations that have a completely different meaning. This is especially the case when rare words occur. Read More

The production of two-jet final states in deep inelastic scattering is an important QCD precision observable. We compute it for the first time to next-to-next-to-leading order (NNLO) in perturbative QCD. Our calculation is fully differential in the lepton and jet variables and allows one to impose cuts on the jets both in the laboratory and the Breit frame. Read More

In this paper we combine the advantages of a model using global source sentence contexts, the Discriminative Word Lexicon, and neural networks. By using deep neural networks instead of the linear maximum entropy model in the Discriminative Word Lexicon models, we are able to leverage dependencies between different source words due to the non-linearity. Furthermore, the models for different target words can share parameters and therefore data sparsity problems are effectively reduced. Read More