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Phrase training based adaptation for statistical machine translation

Phrase training based adaptation for statistical machine tra...

引用

2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2013

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

ISBN: (纸本)9781937284473

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 training on a smaller in-domain set. This way, we bias the probabilities of the general TM towards the in-domain distribution. Experimental results on two different lectures translation tasks show significant improvements of the adapted systems over the general ones. Additionally, we compare our results to mixture modeling, where we report gains when using the suggested phrase training adaptation method. © 2013 Association for Computational Linguistics.

关键词： Probability distributions

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Phrase Training Based Adaptation for Statistical Machine Translation 2

Phrase Training Based Adaptation for Statistical Machine Tra...

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2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2013

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

ISBN: (纸本)9781937284473

关键词： Probability distributions

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A Performance Study of Cube Pruning for Large-Scale Hierarchical Machine Translation 7

A Performance Study of Cube Pruning for Large-Scale Hierarch...

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7th Workshop on Syntax, Semantics and Structure in Statistical Translation, SSST 2013

作者： Huck, Matthias Vilar, David Freitag, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University AachenD-52056 Germany Dfki GmbH Alt-Moabit 91c BerlinD-10559 Germany

ISBN: (纸本)9781937284473

In this paper, we empirically investigate the impact of critical configuration parameters in the popular cube pruning algorithm for decoding in hierarchical statistical machine translation. Specifically, we study how the choice of the k-best generation size affects translation quality and resource requirements in hierarchical search. We furthermore examine the influence of two different granularities of hypothesis recombination. Our experiments are conducted on the large-scale Chinese!English and Arabic!English NIST translation tasks. Besides standard hierarchical grammars, we also explore search with restricted recursion depth of hierarchical rules based on shallow-1 grammars. ©2013 Association for Computational Linguistics.

关键词： Computer aided language translation

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Efficient nearly error-less LVCSR decoding based on incremental forward and backward passes

Efficient nearly error-less LVCSR decoding based on incremen...

引用

IEEE Workshop on Automatic Speech recognition and Understanding

作者： David Nolden Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781479927579

We show that most search errors can be identified by aligning the results of a symmetric forward and backward decoding pass. Based on this knowledge, we introduce an efficient high-level decoding architecture which yields virtually no search errors, and requires virtually no manual tuning. We perform an initial forward- and backward decoding with tight initial beams, then we identify search errors, and then we recursively increment the beam sizes and perform new forward and backward decodings for erroneous intervals until no more search errors are detected. Consequently, each utterance and even each single word is decoded with the smallest beam size required to decode it correctly. On all tested systems we achieve an error rate equal or very close to classical decoding with ideally tuned beam size, but unsupervisedly without specific tuning, and at around 2 times faster runtime. An additional speedup by factor 2 can be achieved by decoding the forward and backward pass in separate threads.

关键词： Decoding Acoustic beams Hidden Markov models Context Acoustics Error analysis Runtime

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Investigations on hessian-free optimization for cross-entropy training of deep neural networks

Investigations on hessian-free optimization for cross-entrop...

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14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013

作者： Wiesler, Simon Li, Jinyu Xue, Jian Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University 52056 Aachen Germany Microsoft Corporation Redmond WA 98052 United States

Context-dependent deep neural network HMMs have been shown to achieve recognition accuracy superior to Gaussian mixture models in a number of recent works. Typically, neural networks are optimized with stochastic gradient descent. On large datasets, stochastic gradient descent improves quickly during the beginning of the optimization. But since it does not make use of second order information, its asymptotic convergence behavior is slow. In regions with pathological curvature, stochastic gradient descent may almost stagnate and thereby falsely indicate convergence. Another drawback of stochastic gradient descent is that it can only be parallelized within minibatches. The Hessian-free algorithm is a second order batch optimization algorithm that does not suffer from these problems. In a recent work, Hessian-free optimization has been applied to a training of deep neural networks according to a sequence criterion. In that work, improvements in accuracy and training time have been reported. In this paper, we analyze the properties of the Hessian-free optimization algorithm and investigate whether it is suited for cross-entropy training of deep neural networks as well. Copyright © 2013 ISCA.

关键词： Deep neural networks

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OPEN VOCABULARY HANDWRITING recognition USING COMBINED WORD-LEVEL AND CHARACTER-LEVEL language MODELS

OPEN VOCABULARY HANDWRITING RECOGNITION USING COMBINED WORD-...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Michal Kozielski David Rybach Stefan Hahn Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781479903573

In this paper, we present a unified search strategy for open vocabulary handwriting recognition using weighted finite state transducers. Additionally to a standard word-level language model we introduce a separate n-gram character-level language model for out-of-vocabulary word detection and recognition. The probabilities assigned by those two models are combined into one Bayes decision rule. We evaluate the proposed method on the IAM database of English handwriting. An improvement from 22.2% word error rate to 17.3% is achieved comparing to the closed-vocabulary scenario and the best published result.

关键词： Handwriting Vocabulary modelling languages search strategies Error analysis Word Bayes Decision Rule handwriting recognition

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Improvements in RWTH's System for Off-Line Handwriting recognition

Improvements in RWTH's System for Off-Line Handwriting Recog...

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International Conference on Document Analysis and recognition

作者： Michal Michał Kozielski Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Rheinisch-Westfalische Technische Hochschule Aachen Aachen Nordrhein-Westfalen DE Human Language Technol. & Pattern Recognition Group RWTH Aachen Univ. Aachen Germany

ISBN: (纸本)9780769549993

In this paper we describe a novel HMM-based system for off-line handwriting recognition. We adapt successful techniques from the domains of large vocabulary speech recognition and image object recognition: moment-based image normalization, writer adaptation, discriminative feature extraction and training, and open-vocabulary recognition. We evaluate those methods and examine their cumulative effect on the recognition performance. The final system outperforms current state-of-the-art approaches on two standard evaluation corpora for English and French handwriting.

关键词： Hidden Markov models Training Handwriting recognition Feature extraction Databases Error analysis Standards

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FEATURE COMBINATION AND STACKING OF RECURRENT AND NON-RECURRENT NEURAL NETWORKS FOR LVCSR

FEATURE COMBINATION AND STACKING OF RECURRENT AND NON-RECURR...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Christian Plahl Michael Kozielski Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University

ISBN: (纸本)9781479903573

This paper investigates the combination of different short-term features and the combination of recurrent and non-recurrent neural networks (NNs) on a Spanish speech recognition task. Several methods exist to combine different feature sets such as concatenation or linear discriminant analysis (LDA). Even though all these techniques achieve reasonable improvements, feature combination by multi-layer perceptrons (MLPs) outperforms all known approaches. We develop the concept of MLP based feature combination further using recurrent neural networks (RNNs). The phoneme posterior estimates derived from an RNN lead to a significant improvement over the result of the MLPs and achieve a 5% relative better word error rate (WER) with much less parameters. Moreover, we improve the system performance further by combining an MLP and an RNN in a hierarchical framework. The MLP benefits from the preprocessing of the RNN. All NNs are trained on phonemes. Nevertheless, the same concepts could be applied using context-dependent states. In addition to the improvements in recognition performance w.r.t. WER, NN based feature combination methods reduce both, the training and the testing complexity. Overall, the systems are based on a single set of acoustic models, together with the training of different NNs.

关键词： Feature combination Multi-layer perceptron Recurrent neural networks Long-short-term-memory Speech recognition recurrent neural nets Speech recognition CSRP3 gene Stacking Neural network System performance Training

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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

ISBN: (纸本)9781479903566

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 is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs. © 2013 IEEE.

关键词： Deep neural networks

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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

ISBN: (纸本)9781479903573

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 optimization algorithms. Stochastic algorithms are fast on large data sets but can not be parallelized well. In our experiments on a broadcast conversations recognition task, stochastic methods yield competitive results after only a short training period, but when spending enough computational resources for parallelization, batch algorithms are competitive with stochastic algorithms. We obtained slight improvements by using a stochastic second order algorithm. Our best log-linear model outperforms the maximum likelihood trained Gaussian mixture model baseline although being ten times smaller.

关键词： discriminative models optimization speech recognition

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