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检索条件"机构=Pattern Recognition and Human Language Technology"

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Improved decipherment of homophonic ciphers

Improved decipherment of homophonic ciphers

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2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Nuhn, Malte Schamper, Julian Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284961

In this paper, we present two improvements to the beam search approach for solving homophonic substitution ciphers presented in Nuhn et al. (2013): An improved rest cost estimation together with an optimized strategy for obtaining the order in which the symbols of the cipher are deciphered reduces the beam size needed to successfully decipher the Zodiac-408 cipher from several million down to less than one hundred: The search effort is reduced from several hours of computation time to just a few seconds on a single CPU. These improvements allow us to successfully decipher the second part of the famous Beale cipher (see (Ward et al., 1885) and e.g. (King, 1993)): Having 182 different cipher symbols while having a length of just 762 symbols, the decipherment is way more challenging than the decipherment of the previously deciphered Zodiac- 408 cipher (length 408, 54 different symbols). To the best of our knowledge, this cipher has not been deciphered automatically before. © 2014 Association for Computational Linguistics.

关键词： Cryptography

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Unsupervised adaptation for statistical machine translation 9

Unsupervised adaptation for statistical machine translation

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9th Workshop on Statistical Machine Translation, WMT 2014 at the 52nd Conference of the Associationfor Computational Linguistics, ACL 2014

作者： Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781941643174

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 the source-language test corpus. We explore unsupervised adaptation, where the source-language test corpus is combined with the corresponding hypotheses generated by the translation system to perform adaptation. We compare unsupervised adaptation to supervised and pseudo supervised adaptation. Our results show that the choice of the adaptation (target) set is crucial for successful application of adaptation methods. Evaluation is conducted over the German-To-English WMT newswire translation task. The experiments show that the unsupervised adaptation method generates the best translation quality as well as generalizes well to unseen test sets. © Proceedings of the Annual Meeting of the Association for Computational Linguistics 2014.

关键词： Machine translation

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Influence of text line segmentation in Handwritten Text recognition

Influence of text line segmentation in Handwritten Text Reco...

引用

International Conference on Document Analysis and recognition

作者： Verónica Romero Joan Andreu Sánchez Vicente Bosch Katrien Depuydt Jesse de Does Pattern Recognition and Human Language Technology Universitat Politècnica de València Camí de Vera Valencia Spain Instituut voor Nederlandse Lexicologie 2300RA Matthias de Vrieshof 2–3 Leiden Netherlands

Text line segmentation is the process by which text lines in a document image are localized and extracted. It is an important step in off-line Handwritten Text recognition (HTR) given that the input of these systems is the line image of the text to be transcribed. A myriad of solutions to the text line segmentation problem have been proposed in the literature. Although these solutions may differ greatly on what is actually applied to perform the segmentation, they can be classified by the level of precision and detail in the final extracted lines. In this paper we study the influence and real needs of different levels of precision and detail in the segmentation solutions in a real HTR task. We test three technics of text line segmentation whose output range from a simple rectangle for each line to a perfect fitted polygon surrounding the detected lines. Experiments have been carried out with a historical collection and results show that good HTR accuracy can be obtained with simple extraction algorithms.

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The rwth aachen german-english machine translation system for WMT 2014 9

The rwth aachen german-english machine translation system fo...

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9th Workshop on Statistical Machine Translation, WMT 2014 at the 52nd Conference of the Associationfor Computational Linguistics, ACL 2014

作者： Peitz, Stephan Wuebker, Joern Freitag, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781941643174

This paper describes the statistical machine translation (SMT) systems developed at RWTH Aachen University for the German!English translation task of the ACL 2014 Eighth Workshop on Statistical Machine Translation (WMT 2014). Both hierarchical and phrase-based SMT systems are applied employing hierarchical phrase reordering and word class language models. For the phrase-based system, we run discriminative phrase training. In addition, we describe our preprocessing pipeline for German!English. © Proceedings of the Annual Meeting of the Association for Computational Linguistics 2014.

关键词： Computer aided language translation

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RWTHLM - The RWTH Aachen University neural network language modeling toolkit 15

RWTHLM - The RWTH Aachen University neural network language ...

引用

15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken languages, INTERSPEECH 2014

作者： Sundermeyer, Martin Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

We present a novel toolkit that implements the long short-term memory (LSTM) neural network concept for language modeling. The main goal is to provide a software which is easy to use, and which allows fast training of standard recurrent and LSTM neural network language models. The toolkit obtains state-of-the-art performance on the standard Treebank corpus. To reduce the training time, BLAS and related libraries are supported, and it is possible to evaluate multiple word sequences in parallel. In addition, arbitrary word classes can be used to speed up the computation in case of large vocabulary sizes. Finally, the software allows easy integration with SRILM, and it supports direct decoding and rescoring of HTK lattices. The toolkit is available for download under an open source license. Copyright © 2014 ISCA.

关键词： Long short-term memory

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Vector space models for phrase-based machine translation 8

Vector space models for phrase-based machine translation

引用

8th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2014

作者： Alkhouli, Tamer Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Spoken Language Processing Group Univ. Paris-Sud France LIMSI/CNRS Orsay France

ISBN: (纸本)9781937284961

This paper investigates the application of vector space models (VSMs) to the standard phrase-based machine translation pipeline. VSMs are models based on continuous word representations embedded in a vector space. We exploit word vectors to augment the phrase table with new inferred phrase pairs. This helps reduce out-of-vocabulary (OOV) words. In addition, we present a simple way to learn bilingually-constrained phrase vectors. The phrase vectors are then used to provide additional scoring of phrase pairs, which fits into the standard log-linear framework of phrase-based statistical machine translation. Both methods result in significant improvements over a competitive in-domain baseline applied to the Arabic-to-English task of IWSLT 2013. © 2014 Association for Computational Linguistics

关键词： Vectors

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Translation modeling with bidirectional recurrent neural networks

Translation modeling with bidirectional recurrent neural net...

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2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Sundermeyer, Martin Alkhouli, Tamer Wuebker, Joern Ney, Hermann Human Language Technology Pattern Recognition Group RWTH Aachen University Aachen Germany Spoken Language Processing Group Univ. Paris-Sud France LIMSI/CNRS Orsay France

ISBN: (纸本)9781937284961

This work presents two different translation models using recurrent neural networks. The first one is a word-based approach using word alignments. Second, we present phrase-based translation models that are more consistent with phrasebased decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of machine translation, allowing us to use the full source sentence in our models, which is also of theoretical interest. We demonstrate that our translation models are capable of improving strong baselines already including recurrent neural language models on three tasks: IWSLT 2013 German!English, BOLT Arabic!English and Chinese!English. We obtain gains up to 1.6% BLEU and 1.7% TER by rescoring 1000-best lists. © 2014 Association for Computational Linguistics.

关键词： Recurrent neural networks

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Cipher type detection

Cipher type detection

引用

2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Nuhn, Malte Knight, Kevin Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Information Sciences Institute University of Southern California United States

ISBN: (纸本)9781937284961

Manual analysis and decryption of enciphered documents is a tedious and error prone work. Often-even after spending large amounts of time on a particular cipher-no decipherment can be found. Automating the decryption of various types of ciphers makes it possible to sift through the large number of encrypted messages found in libraries and archives, and to focus human effort only on a small but potentially interesting subset of them. In this work, we train a classifier that is able to predict which encipherment method has been used to generate a given ciphertext. We are able to distinguish 50 different cipher types (specified by the American Cryptogram Association) with an accuracy of 58.5%. This is a 11.2% absolute improvement over the best previously published classifier. © 2014 Association for Computational Linguistics.

关键词： Cryptography

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The RWTH Large Vocabulary Arabic Handwriting recognition System

The RWTH Large Vocabulary Arabic Handwriting Recognition Sys...

引用

IAPR International Workshop on Document Analysis Systems, DAS

作者： Mahdi Hamdani Patrick Doetsch Michal Kozielski Amr El-Desoky Mousa Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

This paper describes the RWTH system for large vocabulary Arabic handwriting recognition. The recognizer is based on Hidden Markov Models (HMMs) with state of the art methods for visual/language modeling and decoding. The feature extraction is based on Recurrent Neural Networks (RNNs) which estimate the posterior distribution over the character labels for each observation. Discriminative training using the Minimum Phone Error (MPE) criterion is used to train the HMMs. The recognition is done with the help of n-gram language Models (LMs) trained using in-domain text data. Unsupervised writer adaptation is also performed using the Constrained Maximum Likelihood Linear Regression (CMLLR) feature adaptation. The RWTH Arabic handwriting recognition system gave competitive results in previous handwriting recognition competitions. The used techniques allows to improve the performance of the system participating in the OpenHaRT 2013 evaluation.

关键词： Hidden Markov models Handwriting recognition Feature extraction Training Vocabulary Speech recognition Context

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Towards Unsupervised Learning for Handwriting recognition

Towards Unsupervised Learning for Handwriting Recognition

引用

International Workshop on Frontiers in Handwriting recognition

作者： Michal Kozielski Malte Nuhn Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We present a method for training an off-line handwriting recognition system in an unsupervised manner. For an isolated word recognition task, we are able to bootstrap the system without any annotated data. We then retrain the system using the best hypothesis from a previous recognition pass in an iterative fashion. Our approach relies only on a prior language model and does not depend on an explicit segmentation of words into characters. The resulting system shows a promising performance on a standard dataset in comparison to a system trained in a supervised fashion for the same amount of training data.

关键词： Hidden Markov models Training Vocabulary Error analysis Handwriting recognition Training data Ciphers

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