May 08, · In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and with no access to extra-sentential context. In this thesis I propose ways to automatically assess the coherence of machine translation blogger.com by: 1 Thang Luong's thesis on Neural Machine Translation - thesis/blogger.com at master · lmthang/thesis Phd Thesis Machine Translation help you overcome problems connected with understanding its principles/10()
thesis/blogger.com at master · lmthang/thesis · GitHub
The dream of automatic translation that builds the communication bridge between people from different civilizations dates back to thousands of years ago. For the past decades, researchers devoted to proposing practical plans, from rule-based machine translation to statistical machine translation.
In recent years, with the general success of artificial intelligence AI and the emergence of neural network models, a. deep learning, neural machine translation NMTphd thesis machine translation, as the new generation of machine translation framework based on sequence-to-sequence learning has achieved the state-of-the-art and even human-level translation performance on a variety of languages. The impressive achievements brought by NMT are mainly due to its deep neural network structures with massive numbers of parameters, which can be efficiently tuned from vast volume of parallel data in the order of tens or hundreds of millions of sentences.
Unfortunately, phd thesis machine translation, in spite of their success, neural systems also bring about new challenges to machine translation, in which one of the central problems is efficiency.
The efficiency issue involves two aspects: 1 NMT is data-hungry because of its vast size of parameters, phd thesis machine translation, which makes training a reasonable model difficult in practice for low resource cases. For instance, most of the human languages do not have enough parallel data with other languages to learn an NMT model. Moreover, documents in specialized domains such as law or medicine usually contain tons of professional translations, leading to less efficiency for NMT to learn from; 2 NMT is slow in computation compared to conventional methods due to its deep structure and limitations of the decoding algorithms.
Especially the low efficiency at inference time profoundly affects the real-life application and the smoothness of the communication. In some cases like video conference, we also hope the neural system translates at real-time which, however, is difficult for the existing NMT models.
This dissertation attempts to tackle these two challenges. Contributions are twofold: 1 We address the data-efficiency challenges presented by existing NMT models and introduce insights phd thesis machine translation on the characteristics of the data, which includes a developing the copy-mechanism to target on rote memories in translation and general sequence-to-sequence learning; b using a non-parametric search-engine to guide the NMT system to perform well in special domains; c phd thesis machine translation a universal NMT system for extremely low resource languages; d extending the universal NMT system to be able to efficiently adapt to new languages by combing with meta-learning.
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PhD Thesis Defense - Parker Riley
, time: 44:41Coherence in Machine Translation - White Rose eTheses Online
Thang Luong's thesis on Neural Machine Translation - thesis/blogger.com at master · lmthang/thesis phd thesis machine translation Matej Balog, Ilya Tolstikhin, and Bernhard Schölkopf. Differentially private database release via kernel mean blogger.com 35th International Conference on Machine Learning, Stockholm, Sweden, July /10() Phd Thesis On Machine Traslation placing an order using Phd Thesis On Machine Traslation our order form or using Phd Thesis On Machine Traslation our services, you agree to be bound by our terms and conditions. You also agree to use the papers we provide as a general guideline for writing your own paper and to not hold the
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