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N-Gram posterior probabilities for statistical machine translation

N-Gram posterior probabilities for statistical machine trans...

引用

2006 Workshop on Statistical Machine Translation, WMT 2006, collocated with the HLT-NAACL 2006

作者： Zens, Richard Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 - Computer Science Department RWTH Aachen University AachenD-52056 Germany

Word posterior probabilities are a common approach for confidence estimation in automatic speech recognition and machine translation. We will generalize this idea and introduce n-gram posterior probabilities and show how these can be used to improve translation quality. Additionally, we will introduce a sentence length model based on posterior probabilities. We will show significant improvements on the Chinese-English NIST task. The absolute improvements of the BLEU score is between 1.1% and 1.6%. © HLT-NAACL *** right reserved.

关键词： Machine translation

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Creating the best development corpus for Statistical Machine Translation systems 21

Creating the best development corpus for Statistical Machine...

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21st Annual Conference of the European Association for Machine Translation, EAMT 2018

作者： Chinea-Rios, Mara Sanchis-Trilles, Germán Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain Sciling València Spain

ISBN: (纸本)9788409019014

We propose and study three different novel approaches for tackling the problem of development set selection in Statistical Machine Translation. We focus on a scenario where a machine translation system is leveraged for translating a specific test set, without further data from the domain at hand. Such test set stems from a real application of machine translation, where the texts of a specific e-commerce were to be translated. For developing our development-set selection techniques, we first conducted experiments in a controlled scenario, where labelled data from different domains was available, and evaluated the techniques both with classification and translation quality metrics. Then, the best-performing techniques were evaluated on the e-commerce data at hand, yielding consistent improvements across two language directions. © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.

关键词： Machine 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...

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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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Discriminative reordering models for statistical machine translation

Discriminative reordering models for statistical machine tra...

引用

2006 Workshop on Statistical Machine Translation, WMT 2006, collocated with the HLT-NAACL 2006

作者： Zens, Richard Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany

We present discriminative reordering models for phrase-based statistical machine translation. The models are trained using the maximum entropy principle. We use several types of features: based on words, based on word classes, based on the local context. We evaluate the overall performance of the reordering models as well as the contribution of the individual feature types on a word-aligned corpus. Additionally, we show improved translation performance using these reordering models compared to a state-of-the-art baseline system. © HLT-NAACL *** right reserved.

关键词： Computer aided language translation

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Layer-Normalized LSTM for Hybrid-Hmm and End-To-End ASR

Layer-Normalized LSTM for Hybrid-Hmm and End-To-End ASR

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mohammad Zeineldeen Albert Zeyer Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Training deep neural networks is often challenging in terms of training stability. It often requires careful hyperparameter tuning or a pretraining scheme to converge. Layer normalization (LN) has shown to be a crucial ingredient in training deep encoder-decoder models. We explore various LN long short-term memory (LSTM) recurrent neural networks (RNN) variants by applying LN to different parts of the internal recurrency of LSTMs. There is no previous work that investigates this. We carry out experiments on the Switchboard 300h task for both hybrid and end-to-end ASR models and we show that LN improves the final word error rate (WER), the stability during training, allows to train even deeper models, requires less hyperparameter tuning, and works well even without pre-training. We find that applying LN to both forward and recurrent inputs globally, which we denoted by Global Joined Norm variant, gives a 10% relative improvement in WER.

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Efficient approximations to model-based joint tracking and recognition of continuous sign language

Efficient approximations to model-based joint tracking and r...

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International Conference on Automatic Face and Gesture recognition

作者： Philippe Dreuw Jens Forster Thomas Deselaers Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We propose several tracking adaptation approaches to recover from early tracking errors in sign language recognition by optimizing the obtained tracking paths w.r.t. to the hypothesized word sequences of an automatic sign language recognition system. Hand or head tracking is usually only optimized according to a tracking criterion. As a consequence, methods which depend on accurate detection and tracking of body parts lead to recognition errors in gesture and sign language processing. We analyze an integrated tracking and recognition approach addressing these problems and propose approximation approaches over multiple hand hypotheses to ease the time complexity of the integrated approach. Most state-of-the-art systems consider tracking as a preprocessing feature extraction part. Experiments on a publicly available benchmark database show that the proposed methods strongly improve the recognition accuracy of the system.

关键词： Handicapped aids pattern recognition Feature extraction Particle tracking Principal component analysis humans Head Spatial databases Image recognition Fuses

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A Comprehensive Study of Residual CNNS for Acoustic Modeling in ASR

A Comprehensive Study of Residual CNNS for Acoustic Modeling...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Vitalii Bozheniuk Albert Zeyer Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

Long short-term memory (LSTM) networks are the dominant architecture for large vocabulary continuous speech recognition (LVCSR) acoustic modeling due to their good performance. However, LSTMs are hard to tune and computationally expensive. To build a system with lower computational costs and which allows online streaming applications, we explore convolutional neural networks (CNN). To the best of our knowledge there is no overview on CNN hyper-parameter tuning for LVCSR in the literature, so we present our results explicitly. Apart from recognition performance, we focus on the training and evaluation speed and provide a time-efficient setup for CNNs. We faced an overfitting problem in training and solved it with data augmentation, namely SpecAugment. The system achieves results competitive with the top LSTM results. We significantly increased the speed of CNN in training and decoding approaching the speed of the offline LSTM.

关键词： acoustic modeling CNN ResNet LACE dense prediction

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The RWTH Phrase-based Statistical Machine Translation System 2

The RWTH Phrase-based Statistical Machine Translation System

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2nd International Workshop on Spoken language Translation, IWSLT 2005

作者： Zens, Richard Bender, Oliver Hasan, Saša Khadivi, Shahram Matusov, Evgeny Xu, Jia Zhang, Yuqi Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science Department RWTH Aachen University AachenD-52056 Germany

We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation 2005. We use a two pass approach. In the first pass, we generate a list of the N best translation candidates. The second pass consists of rescoring and reranking this N-best list. We will give a description of the search algorithm as well as the models that are used in each pass. We participated in the supplied data tracks for manual transcriptions for the following translation directions: Arabic-English, Chinese-English, English-Chinese and Japanese-English. For Japanese-English, we also participated in the C-Star track. In addition, we performed translations of automatic speech recognition output for Chinese-English and Japanese-English. For both language pairs, we translated the single-best ASR hypotheses. Additionally, we translated Chinese ASR lattices. © 2005 International Workshop on Spoken language Translation, IWSLT 2005.

关键词： Speech recognition

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Optimizing Energies for Pose-Invariant Face recognition

Optimizing Energies for Pose-Invariant Face Recognition

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International Conference on pattern recognition

作者： Harald Hanselmann Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

One of the most difficult challenges in face recognition is the large variation in pose. One approach to handle this problem is to use a 2D-Warping algorithm in a nearest-neighbor classifier. The 2D-Warping algorithm optimizes an energy function that captures the cost of matching pixels between two images while respecting the 2D dependencies defined by local pixel neighborhoods. Optimizing this energy function is an NP-complete problem and is therefore approached with algorithms that aim to approximate the optimal solution. In this paper we compare two algorithms that do this without discarding any 2D dependencies and we study the effect of the quality of the approximate solutions on the classification performance. Additionally, we propose a new algorithm that is capable of finding better solutions and obtaining better energies than the other methods. The experimental evaluation on the CMU-MultiPIE database shows that the proposed algorithm also achieves state-of-the-art recognition accuracies.

关键词： Two dimensional displays Face recognition Approximation algorithms Dynamic programming Face Heuristic algorithms Message passing

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ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding

ELoPE: Fine-Grained Visual Classification with Efficient Loc...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Harald Hanselmann Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

The task of fine-grained visual classification (FGVC) deals with classification problems that display a small inter-class variance such as distinguishing between different bird species or car models. State-of-the-art approaches typically tackle this problem by integrating an elaborate attention mechanism or (part-) localization method into a standard convolutional neural network (CNN). Also in this work the aim is to enhance the performance of a backbone CNN such as ResNet by including three efficient and lightweight components specifically designed for FGVC. This is achieved by using global k-max pooling, a discriminative embedding layer trained by optimizing class means and an efficient localization module that estimates bounding boxes using only class labels for training. The resulting model achieves state-of-the-art recognition accuracies on multiple FGVC benchmark datasets.

关键词： Training Task analysis Automobiles Standards Visualization Birds Testing

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