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Discriminative reordering extensions for hierarchical phrase-based machine translation 16

Discriminative reordering extensions for hierarchical phrase...

引用

16th Annual Conference of the European Association for Machine Translation, EAMT 2012

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

In this paper, we propose novel extensions of hierarchical phrase-based systems with a discriminative lexicalized reordering model. We compare different feature sets for the discriminative reordering model and investigate combinations with three types of non-lexicalized reordering rules which are added to the hierarchical grammar in order to allow for more reordering flexibility during decoding. All extensions are evaluated in standard hierarchical setups as well as in setups where the hierarchical recursion depth is restricted. We achieve improvements of up to +1.2 %BLEU on a large-scale Chinese→English translation task. © 2012 European Association for Machine Translation.

关键词： Hierarchical systems

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Investigation of mixture splitting concept for training linear bottlenecks of deep neural network acoustic models 40

Investigation of mixture splitting concept for training line...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Tahir, Muhammad Ali Wiesler, Simon Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Germany Spoken Language Processing Group LIMSI CNRS Paris France

ISBN: (纸本)9781467369978

A Gaussian or log-linear mixture model trained by maximum likelihood may be trained further using discriminative training. It is desirable that the mixture splitting is also done during the discriminative training, to achieve better mixture density distribution. In previous work such a discriminative splitting approach was presented. Similarly, the resolution of a deep neural network may also be increased by splitting. In this paper, discriminative splitting is applied as a way of initializing a linear bottleneck between two layers of a DNN. Experiments for a single hidden layer and six hidden layer cases show the potential of this approach as an alternative method of pre-training for linear bottlenecks for MLP hidden layers. © 2015 IEEE.

关键词： deep neural network linear bottleneck mixture splitting

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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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Improved strategies for a zero oov rate LVCSR system 40

Improved strategies for a zero oov rate LVCSR system

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Shaik, M. Ali Basha Mousa, Amr El-Desoky Hahn, Stefan Schluter, 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

ISBN: (纸本)9781467369978

In this work, multiple hierarchical language modeling strategies for a zero OOV rate large vocabulary continuous speech recognition system are investigated. In our previously proposed hierarchical approach, a full-word language model and a context independent character-level LM (CLM) are directly used during search. The novelty of this work is to jointly model the character-level prior and the pronunciation probabilities, to introduce across-word context into the characterlevel LM, and to properly normalize the character-level LM using prefix-tree based normalization for the hierarchical approach. Significant reductions in-terms of word error rates (WER) on the best full-word Quaero Polish LVCSR system are reported. © 2015 IEEE.

关键词： hierarchical LVCSR OOV prefix-tree

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Improved Chunk-level Reordering for Statistical Machine Translation 4

Improved Chunk-level Reordering for Statistical Machine Tran...

引用

4th International Workshop on Spoken language Translation, IWSLT 2007

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

Inspired by previous chunk-level reordering approaches to statistical machine translation, this paper presents two methods to improve the reordering at the chunk level. By introducing a new lattice weighting factor and by reordering the training source data, an improvement is reported on TER and BLEU. Compared to the previous chunk-level reordering approach, the BLEU score improves 1.4% absolutely. The translation results are reported on IWSLT Chinese-English task. © 2007 International Workshop on Spoken language Translation, IWSLT 2007. All rights reserved.

关键词： Computer aided language translation

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Investigations on sequence training of neural networks 40

Investigations on sequence training of neural networks

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Wiesler, Simon Golik, Pavel Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany LIMSI CNRS Spoken Language Processing Group Paris France

ISBN: (纸本)9781467369978

In this paper we present an investigation of sequence-discriminative training of deep neural networks for automatic speech recognition. We evaluate different sequence-discriminative training criteria (MMI and MPE) and optimization algorithms (including SGD and Rprop) using the RASR toolkit. Further, we compare the training of the whole network with that of the output layer only. Technical details necessary for a robust training are studied, since there is no consensus yet on the ultimate training recipe. The investigation extends our previous work on training linear bottleneck networks from scratch showing the consistently positive effect of sequence training. © 2015 IEEE.

关键词： deep neural networks optimization sequence training speech recognition

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Sequence-discriminative training of recurrent neural networks 40

Sequence-discriminative training of recurrent neural network...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Voigtlaender, Paul Doetsch, Patrick Wiesler, Simon Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany LIMSI CNRS Spoken Language Processing Group Paris France

ISBN: (纸本)9781467369978

We investigate sequence-discriminative training of long shortterm memory recurrent neural networks using the maximum mutual information criterion. We show that although recurrent neural networks already make use of the whole observation sequence and are able to incorporate more contextual information than feed forward networks, their performance can be improved with sequence-discriminative training. Experiments are performed on two publicly available handwriting recognition tasks containing English and French handwriting. On the English corpus, we obtain a relative improvement in WER of over 11% with maximum mutual information (MMI) training compared to cross-entropy training. On the French corpus, we observed that it is necessary to interpolate the MMI objective function with cross-entropy. © 2015 IEEE.

关键词： handwriting recognition long shortterm memory recurrent neural networks sequence-discriminative training

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Feature-rich sub-lexical language models using a maximum entropy approach for German LVCSR

Feature-rich sub-lexical language models using a maximum ent...

引用

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

作者： Shaik, M. Ali Basha El-Desoky Mousa, Amr Schlüter, Ralf Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

German is a morphologically rich language having a high degree of word inflections, derivations and compounding. This leads to high out-of-vocabulary (OOV) rates and poor language model (LM) probabilities in the large vocabulary continuous speech recognition (LVCSR) systems. One of the main challenges in the German LVCSR is the recognition of the OOV words. For this purpose, data-driven morphemes are used to provide higher lexical coverage. On the other hand, the probability estimates of a sub-lexical LM could be further improved using feature-rich LMs like maximum entropy (MaxEnt) and class-based LMs. In this work, for a sub-lexical level German LVCSR task, we investigate the use of the multiple morpheme level features as classes for building class-based LMs that are estimated using the state-of-the-art MaxEnt approach. Thus, the benefits of both the MaxEnt LMs and the traditional class-based LMs are effectively combined. Furthermore, we experiment the use of Maximum a-posteriori adaptation over the MaxEnt class-based LMs. We show consistent reductions in both the OOV recognition error rate and the word error rate (WER) on a German LVCSR task from the Quaero project, compared to the traditional class-based and the N-gram morpheme based LM. Copyright © 2013 ISCA.

关键词： Computational linguistics

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Morpheme level hierarchical pitman-Yor class-based language models for LVCSR of morphologically rich languages

Morpheme level hierarchical pitman-Yor class-based language ...

引用

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

作者： El-Desoky Mousa, Amr Shaik, M. Ali Basha Schlüter, Ralf Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

Performing large vocabulary continuous speech recognition (LVCSR) for morphologically rich languages is considered a challenging task. The morphological richness of such languages leads to high out-of-vocabulary (OOV) rates and poor language model (LM) probabilities. In this case, the use of morphemes has been shown to increase the lexical coverage and lower the LM perplexity. Another approach used to improve the LM probability estimates is to incorporate additional knowledge sources in the LM estimation process using classbased LMs (CLMs). Recently, the hierarchical Pitman-Yor LMs (HPYLMs) have shown superiority over the modified Kneser- Ney (MKN) smoothed N-gram LMs in terms of both perplexity (PPL) and word error rate (WER) on word-based LVCSR tasks. In this paper, hierarchical Pitman-Yor class-based LMs (HPYCLMs) are combined with morpheme level language modeling. This enables the application of the proposed models on top of morpheme-based systems. Experiments are conducted on Arabic and German LVCSR tasks. Consistent performance improvements are obtained for all the available corpora compared to the conventional morpheme-based and class-based LMs. Copyright © 2013 ISCA.

关键词： Modeling languages

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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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