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The RWTH Aachen Machine Translation System for IWSLT 2011 8

The RWTH Aachen Machine Translation System for IWSLT 2011

引用

8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Wuebker, Joern Huck, Matthias Mansour, Saab Freitag, Markus Feng, Minwei Peitz, Stephan Schmidt, Christoph Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

In this paper the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2011 is presented. We participated in the MT (English-French, Arabic-English, Chinese-English) and SLT (English-French) tracks. Both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated, including domain adaptation via monolingual and bilingual data selection, phrase training, different lexical smoothing methods, additional reordering models for the hierarchical system, various Arabic and Chinese segmentation methods, punctuation prediction for speech recognition output, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems. © IWSLT 2011. All rights reserved.

关键词： Hierarchical systems

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Lightly-Supervised Training for Hierarchical Phrase-Based Machine Translation 1

Lightly-Supervised Training for Hierarchical Phrase-Based Ma...

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1st Workshop on Unsupervised Learning in NLP at the 2011 Conference on Empirical Methods in Natural language Processing, EMNLP 2011

作者： Huck, Matthias Vilar, David Stein, Daniel Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany DFKI GmbH Berlin Germany

ISBN: (纸本)1937284131

In this paper we apply lightly-supervised training to a hierarchical phrase-based statistical machine translation system. We employ bitexts that have been built by automatically translating large amounts of monolingual data as additional parallel training corpora. We explore different ways of using this additional data to improve our system. Our results show that integrating a second translation model with only non-hierarchical phrases extracted from the automatically generated bitexts is a reasonable approach. The translation performance matches the result we achieve with a joint extraction on all training bitexts while the system is kept smaller due to a considerably lower overall number of phrases. © 2011 Association for Computational Linguistics

关键词： Computer aided language translation

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Advancements in Arabic-to-English hierarchical machine translation

Advancements in Arabic-to-English hierarchical machine trans...

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15th International Conference of the European Association for Machine Translation, EAMT 2011

作者： Huck, Matthias Vilar, David Stein, Daniel Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany DFKI GmbH Berlin Germany

ISBN: (纸本)9789081486118

In this paper we study several advanced techniques and models for Arabic-to-English statistical machine translation. We examine how the challenges imposed by this particular language pair and translation direction can be successfully tackled within the framework of hierarchical phrase-based translation. We extend the state-of-the-art with a novel cross-system and cross-paradigm lightlysupervised training approach. In addition, for following recently developed techniques we provide a concise review, an empirical evaluation, and an in-depth analysis: soft syntactic labels, a discriminative word lexicon model, additional reorderings, and shallow rules. We thus bring together complementary methods that previously have only been investigated in isolation and mostly on different language pairs. Combinations of the methods yield significant improvements over a baseline using a usual set of models. The resulting hierarchical systems perform competitive on the large-scale NIST Arabic-to-English translation task. © 2011 European Association for Machine Translation.

关键词： Hierarchical systems

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Soft String-to-Dependency Hierarchical Machine Translation 8

Soft String-to-Dependency Hierarchical Machine Translation

引用

8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Peter, Jan-Thorsten Huck, Matthias Ney, Hermann Stein, Daniel Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Fraunhofer IAIS St. Augustin Germany

In this paper, we dissect the influence of several target-side dependency-based extensions to hierarchical machine translation, including a dependency language model (LM). We pursue a non-restrictive approach that does not prohibit the production of hypotheses with malformed dependency structures. Since many questions remained open from previous and related work, we offer in-depth analysis of the influence of the language model order, the impact of dependency-based restrictions on the search space, and the information to be gained from dependency tree building during decoding. The application of a non-restrictive approach together with an integrated dependency LM scoring is a novel contribution which yields significant improvements for two large-scale translation tasks for the language pairs Chinese-English and German-French. © IWSLT 2011. All rights reserved.

关键词： Machine translation

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Warp that smile on your face: Optimal and smooth deformations for face recognition

Warp that smile on your face: Optimal and smooth deformation...

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作者： Gass, Tobias Pishchulin, Leonid Dreuw, Philippe Ney, Hermann Computer Vision Laboratory ETH Zurich Switzerland Computer Vision and Multimodal Computing MPI Informatics Saarbruecken Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781424491407

In this work, we present novel warping algorithms for full 2D pixel-grid deformations for face recognition. Due to high variation in face appearance, face recognition is considered a very difficult task, especially if only a single reference image, for example a mug-shot, per face is available. Usually model-based approaches with additional training data are used to cope with several types of variation occurring in facial imaging. Image warping contrarily yields a distance measure which is invariant with regard to several types of variation. This allows for precise recognition even using only very few reference observations. Due to the computationally complex problem of optimal 2D warping, pseudo-2D warping-based approaches in the past represented strong approximations of the original problem, and were mainly successful on data with low variability or rectified images. We propose a novel 2D warping method which is globally optimal and makes no prior assumtions on the data variability besides two-dimensional smootheness constraints which both avoid local mirroring and gaps and significantly speed up the optimization. Furthermore, we show that occlusion handling is imperative to obtain smooth warpings in a variety of domains. We evaluate our novel algorithm on various well known databases, such as the AR-Face and CMU-PIE database, and provide a detailed comparison to existing warping approaches. We show that by using simple relative 2D constraints, strong local features and a kernel, which is robust w.r.t. occlusions, our computationally complex approaches outperform state-of-the-art results for recognizing faces under varying expressions, occlusions and poses. Most interestingly, we achieve higher accuracy using fewer training instances per class compared to methods learning a model of the 3D shape. © 2011 IEEE.

关键词： Face recognition

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Using morpheme and syllable based sub-words for polish LVCSR

Using morpheme and syllable based sub-words for polish LVCSR

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： M. Ali Basha Shaik Amr El-Desoky Mousa Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

Polish is a synthetic language with a high morpheme-per-word ratio. It makes use of a high degree of inflection leading to high out-of-vocabulary (OOV) rates, and high language Model (LM) perplexities. This poses a challenge for Large Vocabulary and Continuous Speech recognition (LVCSR) systems. Here, the use of morpheme and syllable based units is investigated for building sub-lexical LMs. A different type of sub-lexical units is proposed based on combining morphemic or syllabic units with corresponding pronunciations. Thereby, a set of grapheme-phoneme pairs called graphones are used for building LMs. A relative reduction of 3.5% in Word Error Rate (WER) is obtained with respect to a traditional system based on full-words.

关键词： Vocabulary Speech recognition Speech Adaptation models Computational modeling Error analysis Joints

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EXPLOITING SPARSENESS OF BACKING-OFF language MODELS FOR EFFICIENT LOOK-AHEAD IN LVCSR

EXPLOITING SPARSENESS OF BACKING-OFF LANGUAGE MODELS FOR EFF...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： David Nolden Hermann Ney Ralf Schluter Human Language Technology and Pattern Recognition GroupRWTH Aachen University Aachen Germany

ISBN: (纸本)9781457705380

In this paper, we propose a new method for computing and applying language model look-ahead in a dynamic network decoder, exploiting the sparseness of backing-off n-gram language models. Only partial (sparse) look-ahead tables are computed, with a size that depends on the number of words that have an n-gram score in the language model for a specific context, rather than a constant, vocabulary dependent size. Since high order backing-off language models are inherently sparse, this mechanism reduces the runtime- and memory effort of computing the look-ahead tables by magnitudes. A modified decoding algorithm is required to apply these sparse LM look-ahead tables efficiently. We show that sparse LM look-ahead is much more efficient than the classical method, and that full n-gram look-ahead becomes favorable over lower order look-ahead even when many distinct LM contexts appear during decoding.

关键词： speech recognition decoding search language model look-ahead modelling languages Tables language DECODING indium new methods dynamic network WWOX gene decoding algorithm

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THE RWTH 2010 QUAERO ASR EVALUATION SYSTEM FOR ENGLISH, FRENCH, AND GERMAN

THE RWTH 2010 QUAERO ASR EVALUATION SYSTEM FOR ENGLISH, FREN...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： M. Sundermeyer M. Nussbaum-Thom S. Wiesler C. Plahl A. El-Desoky Mousa S. Hahn D. Nolden R. Schluter H. Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University

ISBN: (纸本)9781457705380

Recognizing Broadcast Conversational (BC) speech data is a difficult task, which can be regarded as one of the major challenges beyond the recognition of Broadcast News (BN). This paper presents the automatic speech recognition systems developed by RWTH for the English, French, and German language which attained the best word error rates for English and German, and competitive results for the French task in the 2010 Quaero evaluation for BC and BN data. At the same time, the RWTH German system used the least amount of training data among all participants. Large reductions in word error rate were obtained by the incorporation of the new Bottleneck Multilayer Perceptron (MLP) features for all three languages. Additional improvements were obtained for the German system by applying a new language modeling technique, decomposing words into sublexical components.

关键词： automatic speech recognition multilayer perceptrons

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A convergence analysis of log-linear training and its application to speech recognition

A convergence analysis of log-linear training and its applic...

引用

IEEE Workshop on Automatic Speech recognition and Understanding

作者： S. Wiesler R. Schlüter H. Ney Human Language Technology and Pattern Recognition RWTH Aachen University of Technology Aachen Germany

Log-linear models are a promising approach for speech recognition. Typically, log-linear models are trained according to a strictly convex criterion. Optimization algorithms are guaranteed to converge to the unique global optimum of the objective function from any initialization. For large-scale applications, considerations in the limit of infinite iterations are not sufficient. We show that log-linear training can be a highly ill-conditioned optimization problem, resulting in extremely slow convergence. Conversely, the optimization problem can be preconditioned by feature transformations. Making use of our convergence analysis, we improve our log-linear speech recognition system and achieve a strong reduction of its training time. In addition, we validate our analysis on a continuous handwriting recognition task.

关键词： Hidden Markov models Training Optimization Convergence Speech recognition Eigenvalues and eigenfunctions Polynomials

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A comparative analysis of dynamic network decoding

A comparative analysis of dynamic network decoding

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： David Rybach Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781457705380

The use of statically compiled search networks for ASR systems using huge vocabularies and complex language models often becomes challenging in terms of memory requirements. Dynamic network decoders introduce additional computations in favor of significantly lower memory consumption. In this paper we investigate the properties of two well-known search strategies for dynamic network decoding, namely history conditioned tree search and WFST-based search using dynamic transducer composition. We analyze the impact of the differences in search graph representation, search space structure, and language model look-ahead techniques. Experiments on an LVCSR task illustrate the influence of the compared properties.

关键词： Decoding Transducers Hidden Markov models History Speech recognition Context Vocabulary

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