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检索条件"机构=Human Language Technology and Pattern Recognition Group Computer Science"
214 条 记 录,以下是111-120 订阅
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Improving Long Context Document-Level Machine Translation
arXiv
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arXiv 2023年
作者: Herold, Christian Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany
Document-level context for neural machine translation (NMT) is crucial to improve the translation consistency and cohesion, the translation of ambiguous inputs, as well as several other linguistic phenomena. Many work... 详细信息
来源: 评论
Is Encoder-Decoder Redundant for Neural Machine Translation?
arXiv
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arXiv 2022年
作者: Gao, Yingbo Herold, Christian Yang, Zijian Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany
Encoder-decoder architecture is widely adopted for sequence-to-sequence modeling tasks. For machine translation, despite the evolution from long short-term memory networks to Transformer networks, plus the introductio... 详细信息
来源: 评论
Exploring Kernel functions in the softmax layer for contextual word classification
arXiv
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arXiv 2019年
作者: Gao, Yingbo Herold, Christian Wang, Weiyue Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany
Prominently used in support vector machines and logistic regressions, kernel functions (kernels) can implicitly map data points into high dimensional spaces and make it easier to learn complex decision boundaries. In ... 详细信息
来源: 评论
On the alignment problem in multi-head attention-based neural machine translation
arXiv
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arXiv 2018年
作者: Alkhouli, Tamer Bretschner, Gabriel Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany
This work investigates the alignment problem in state-of-the-art multi-head attention models based on the transformer architecture. We demonstrate that alignment extraction in transformer models can be improved by aug... 详细信息
来源: 评论
Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic
Morpheme-based feature-rich language models using Deep Neura...
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2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
作者: El-Desoky Mousa, Amr Kuo, Hong-Kwang Jeff Mangu, Lidia Soltau, Hagen Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany IBM T. J. Watson Research Center Yorktown Heights NY 10598 United States
Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech recognition (LVCSR). Building LMs on morpheme level... 详细信息
来源: 评论
Performance analysis of Neural Networks in combination with n-gram language models
Performance analysis of Neural Networks in combination with ...
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Ilya Oparin Martin Sundermeyer Hermann Ney Jean-Luc Gauvain LIMSI CNRS Spoken Language Processing Group France Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Germany
Neural Network language models (NNLMs) have recently become an important complement to conventional n-gram language models (LMs) in speech-to-text systems. However, little is known about the behavior of NNLMs. The ana... 详细信息
来源: 评论
Improvement of Context Dependent Modeling for Arabic Handwriting recognition
Improvement of Context Dependent Modeling for Arabic Handwri...
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International Workshop on Frontiers in Handwriting recognition
作者: Mahdi Hamdani Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
This paper proposes the improvement of context dependent modeling for Arabic handwriting recognition. Since the number of parameters in context dependent models is huge, CART trees are used for state tying. This work ... 详细信息
来源: 评论
Returnn: The RWTH extensible training framework for universal recurrent neural networks
Returnn: The RWTH extensible training framework for universa...
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IEEE International Conference on Acoustics, Speech and Signal Processing
作者: Patrick Doetsch Albert Zeyer Paul Voigtlaender Ilia Kulikov Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52062 Germany
In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network to... 详细信息
来源: 评论
A CRITICAL EVALUATION OF STOCHASTIC ALGORITHMS FOR CONVEX OPTIMIZATION
A CRITICAL EVALUATION OF STOCHASTIC ALGORITHMS FOR CONVEX OP...
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IEEE International Conference on Acoustics, Speech, and Signal Processing
作者: Simon Wiesler Alexander Richard Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
Log-linear models find a wide range of applications in pattern recognition. The training of log-linear models is a convex optimization problem. In this work, we compare the performance of stochastic and batch optimiza... 详细信息
来源: 评论
Powerful extensions to CRFS for grapheme to phoneme conversion
Powerful extensions to CRFS for grapheme to phoneme conversi...
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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Stefan Hahn Patrick Lehnen Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany
Conditional Random Fields (CRFs) have proven to per form well on natural language processing tasks like name transliteration, concept tagging or grapheme-to-phoneme (g2p) conversion. The aim of this paper is to propos... 详细信息
来源: 评论