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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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MorphTagger: HMM-Based Arabic Segmentation for Statistical Machine Translation 7

MorphTagger: HMM-Based Arabic Segmentation for Statistical M...

引用

7th International Workshop on Spoken language Translation, IWSLT 2010

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

In this paper, we investigate different methodologies of Arabic segmentation for statistical machine translation by comparing a rule-based segmenter to different statistically-based segmenters. We also present a new method for segmentation that serves the need for a real-time translation system without impairing the translation accuracy. © IWSLT 2010. All rights reserved.

关键词： Computer aided language translation

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A Simple and Effective Weighted Phrase Extraction for Machine Translation Adaptation 9

A Simple and Effective Weighted Phrase Extraction for Machin...

引用

9th International Workshop on Spoken language Translation, IWSLT 2012

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

The task of domain-adaptation attempts to exploit data mainly drawn from one domain (e.g. news) to maximize the performance on the test domain (e.g. weblogs). In previous work, weighting the training instances was used for filtering dissimilar data. We extend this by incorporating the weights directly into the standard phrase training procedure of statistical machine translation (SMT). This allows the SMT system to make the decision whether to use a phrase translation pair or not, a more methodological way than discarding phrase pairs completely when using filtering. Furthermore, we suggest a combined filtering and weighting procedure to achieve better results while reducing the phrase table size. The proposed methods are evaluated in the context of Arabic-to-English translation on various conditions, where significant improvements are reported when using the suggested weighted phrase training. The weighting method also improves over filtering, and the combined filtering and weighting is better than a standalone filtering method. Finally, we experiment with mixture modeling, where additional improvements are reported when using weighted phrase extraction over a variety of baselines. © IWSLT 2012. All rights reserved.

关键词： Machine translation

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A neural, interactive-predictive system for multimodal sequence to sequence tasks 57

A neural, interactive-predictive system for multimodal seque...

引用

57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, ACL 2019

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politcnica de Valncia Valncia Spain

ISBN: (纸本)9781950737499

We present a demonstration of a neural interactive-predictive system for tackling mul- timodal sequence to sequence tasks. The sys- tem generates text predictions to different se- quence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The sys- tem reacts to each correction, providing alter- native hypotheses, compelling with the feed- back provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client-server architecture. For accessing the system, we developed a website, which com- municates with the neural model, hosted in a local server. From this website, the dif- ferent tasks can be tackled following the interactive-predictive framework. We open- source all the code developed for building this system. The demonstration in hosted in http://***/ interactive-seq2seq. © 2019 All rights reserved.

关键词： Websites

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Combining Translation and language Model Scoring for Domain-Specific Data Filtering 8

Combining Translation and Language Model Scoring for Domain-...

引用

8th International Workshop on Spoken language Translation, IWSLT 2011

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

The increasing popularity of statistical machine translation (SMT) systems is introducing new domains of translation that need to be tackled. As many resources are already available, domain adaptation methods can be applied to utilize these recourses in the most beneficial way for the new domain. We explore adaptation via filtering, using the cross-entropy scores to discard irrelevant sentences. We focus on filtering for two important components of an SMT system, namely the language model (LM) and the translation model (TM). Previous work has already applied LM cross-entropy based scoring for filtering. We argue that LM cross-entropy might be appropriate for LM filtering, but not as much for TM filtering. We develop a novel filtering approach based on a combined TM and LM cross-entropy scores. We experiment with two large-scale translation tasks, the Arabic-to-English and English-to-French IWSLT 2011 TED Talks MT tasks. For LM filtering, we achieve strong perplexity improvements which carry over to the translation quality with improvements up to +0.4% BLEU. For TM filtering, the combined method achieves small but consistent improvements over the standalone methods. As a side effect of adaptation via filtering, the fully fledged SMT system vocabulary size and phrase table size are reduced by a factor of at least 2 while up to +0.6% BLEU improvement is observed. © IWSLT 2011. All rights reserved.

关键词： Computer aided language translation

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Improved decipherment of homophonic ciphers

Improved decipherment of homophonic ciphers

引用

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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Beam search for solving substitution ciphers

Beam search for solving substitution ciphers

引用

51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

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)9781937284503

In this paper we address the problem of solving substitution ciphers using a beam search approach. We present a conceptually consistent and easy to implement method that improves the current state of the art for decipherment of substitution ciphers and is able to use high order n-gram language models. We show experiments with 1:1 substitution ciphers in which the guaranteed optimal solution for 3-gram language models has 38.6% decipherment error, while our approach achieves 4.13% decipherment error in a fraction of time by using a 6-gram language model. We also apply our approach to the famous Zodiac-408 cipher and obtain slightly better (and near to optimal) results than previously published. Unlike the previous state-of-the-art approach that uses additional word lists to evaluate possible decipherments, our approach only uses a letterbased 6-gram language model. Furthermore we use our algorithm to solve large vocabulary substitution ciphers and improve the best published decipherment error rate based on the Gigaword corpus of 7.8% to 6.0% error rate. © 2013 Association for Computational Linguistics.

关键词： Errors

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The RWTH Aachen Machine Translation system for IWSLT 2010 7

The RWTH Aachen Machine Translation system for IWSLT 2010

引用

7th International Workshop on Spoken language Translation, IWSLT 2010

作者： Mansour, Saab Peitz, Stephan Vilar, David Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

In this paper we describe the statistical machine translation system of the RWTH Aachen University developed for the translation task of the IWSLT 2010. This year, we participated in the BTEC translation task for the Arabic to English language direction. We experimented with two state-of-the-art decoders: phrase-based and hierarchical-based decoders. Extensions to the decoders included phrase training (as opposed to heuristic phrase extraction) for the phrase-based decoder, and soft syntactic features for the hierarchical decoder. Additionally, we experimented with various rule-based and statistical-based segmenters for Arabic. Due to the different decoders and the different methodologies that we apply for segmentation, we expect that there will be complimentary variation in the results achieved by each system. The next step would be to exploit these variations and achieve better results by combining the systems. We try different strategies for system combination and report significant improvements over the best single system. © IWSLT 2010. All rights reserved.

关键词： Computational linguistics

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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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