In this paper we address the problem of solving substitution ciphers using a beam search approach. We present a conceptually consistent and easy to implement method that improves the current state of the art for decip...
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The neural hidden Markov model has been proposed as an alternative to attention mechanism in machine translation with recurrent neural networks. However, since the introduction of the transformer models, its performan...
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In this paper we describe the statistical machine translation system of the RWTH Aachen University developed for the translation task of the IWSLT 2010. This year, we participated in the BTEC translation task for the ...
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In this paper we show that even for the case of 1:1 substitution ciphers-which encipher plaintext symbols by exchanging them with a unique substitute-finding the optimal decipherment with respect to a bigram language ...
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In this paper, we propose a novel reordering model based on sequence labeling techniques. Our model converts the reordering problem into a sequence labeling problem, i.e. a tagging task. Results on five Chinese-Englis...
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We present an approach using Gaussian mixture models for part-based object recognition where spatial relationships of the parts are explicitly modeled and parameters of the generative model are tuned discriminatively....
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ISBN:
(纸本)1904410146
We present an approach using Gaussian mixture models for part-based object recognition where spatial relationships of the parts are explicitly modeled and parameters of the generative model are tuned discriminatively. These extensions lead to great improvements of the classification accuracy. Furthermore we evaluate several improvements over our baseline system which incrementally improve the obtained results which compare favorable well to other published results for the three Caltech tasks and the PASCAL evaluation 05 tasks.
In spoken language translation a machine translation system takes speech as input and translates it into another language. A standard machine translation system is trained on written language data and expects written ...
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In this work, we tackle the problem of language and translation models domainadaptation without explicit bilingual indomain training data. In such a scenario, the only information about the domain can be induced from ...
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We present a novel approach for translation model (TM) adaptation using phrase training. The proposed adaptation procedure is initialized with a standard general-domain TM, which is then used to perform phrase trainin...
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This work systematically analyzes the smoothing effect of vocabulary reduction for phrase translation models. We extensively compare various word-level vocabularies to show that the performance of smoothing is not sig...
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