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Online learning of log-linear weights in interactive machine translation

Online learning of log-linear weights in interactive machine...

引用

Joint 7th Spanish Speech technology Workshop and the Iberian SLTech Workshop, IberSPEECH 2012

作者： López-Salcedo, Francisco-Javier Sanchis-Trilles, Germán Casacuberta, Francisco Pattern Recognition and Human Language Technologies Group Departamento de Sistemas Informáticos Y Computación Universitat Politècnica de València Spain

ISBN: (纸本)9783642352911

Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In this environment, the human translator is generating bilingual data after each translation has been marked as correct, and expects the system to be able to learn from the errors made. In this paper, we analyse the appropriateness of discriminative ridge regression for adapting the scaling factors of a state-of-the-art machine translation system within a conventional post-editing scenario and also within an interactive machine translation setup. Results show that the strategies applied in the former setup cannot be directly applied in the latter framework. Hence, the discriminative ridge regression is revised and adapted for the interactive machine translation framework, with encouraging results. © 2012 Springer-Verlag.

关键词： Machine translation

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The CTC and its intriguing "blank" label: Comparative study of neural network training methods for handwriting recognition

The CTC and its intriguing "blank" label: Comparative study ...

引用

Conference en Recherche d'Informations et Applications, CORIA 2016, 13th French Information Retrieval Conference, CIFED 2016, et Colloque International Francophone sur l'Ecrit et le Document - Conference on Information Retrieval and its Applications, CORIA 2016, 13th French Information Retrieval Conference, CIFED 2016, and International French-Speaking Colloquium on Writing and Documentation

作者： Bluche, Théodore Kermorvant, Christopher Ney, Hermann Louradour, Jérôme A2iA SA Paris France Teklia SASU Paris France Human Language Technology and Pattern Recognition RWTH Aachen Allemagne Germany

In recent years, Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) trained with the Connectionist Temporal Classification (CTC) objective won many international handwriting recognition evaluations. The CTC algorithm is based on a forward-backward procedure, avoiding the need of a segmentation of the input before training. The network outputs are characters labels, and a special non-character label. On the other hand, in the hybrid Neural Network / Hidden Markov Models (NN/HMM) framework, networks are trained with framewise criteria to predict state labels. In this paper, we show that CTC training is close to forward-backward training of NN/HMMs, and can be extended to more standard HMM topologies. We apply this method to Multi-Layer Perceptrons (MLPs), and investigate the properties of CTC, especially the role of the special label.

关键词： Character recognition

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Robust online multi-channel speech recognition

Robust online multi-channel speech recognition

引用

12. ITG-Fachtagung Sprachkommunikation - 12th ITG Conference on Speech Communication

作者： Kitza, Markus Zeyer, Albert Schlüter, Ralf Heymann, Jahn Haeb-Umbach, Reinhold Human Language Technology and Pattern Recognition RWTH Aachen Aachen52074 Germany Department of Communications Engineering Paderborn Paderborn University Paderborn33098 Germany

ISBN: (纸本)9783800742752

In this paper we present a system for robust online far-field multi-channel speech recognition with minimal assumptions on microphone configuration and target location. We employ an online-enabled Generalized Eigenvalue (GEV) beamformer and a Long Short-Term Memory (LSTM) network to robustly calculate the signal statistics necessary for the beamforming operation in the front-end. After multiple channels have been condensed to one, a Bidirectional Long Short-Term Memory (BLSTM) acoustic model is applied on a running window of input speech. This enables online decoding in combination with the beamforming front-end. To assess the performance of the system we test it on the real evaluation set of the CHiME 3 data where we achieve a Word Error Rate (WER) of 10.4 %. © 2016 VDE VERLAG GMBH.

关键词： Speech recognition

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Improved training of end-to-end attention models for speech recognition 19

Improved training of end-to-end attention models for speech ...

引用

19th Annual Conference of the International Speech Communication, INTERSPEECH 2018

作者： Zeyer, Albert Irie, Kazuki Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52062 Germany AppTek United States NNAISENSE Switzerland

Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition. In this work, we show that such models can achieve competitive results on the Switchboard 300h and LibriSpeech 1000h tasks. In particular, we report the state-of-the-art word error rates (WER) of 3.54% on the dev-clean and 3.82% on the test-clean evaluation subsets of LibriSpeech. We introduce a new pretraining scheme by starting with a high time reduction factor and lowering it during training, which is crucial both for convergence and final performance. In some experiments, we also use an auxiliary CTC loss function to help the convergence. In addition, we train long short-term memory (LSTM) language models on subword units. By shallow fusion, we report up to 27% relative improvements in WER over the attention baseline without a language model. © 2018 International Speech Communication Association. All rights reserved.

关键词： Speech recognition

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Fast and scalable decoding with language model look-ahead for phrase-based statistical machine translation

Fast and scalable decoding with language model look-ahead fo...

引用

50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Wuebker, Joern Ney, Hermann Zens, Richard Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Google Inc. 1600 Amphitheatre Parkway Mountain View CA 94043 United States

ISBN: (纸本)9781937284251

In this work we present two extensions to the well-known dynamic programming beam search in phrase-based statistical machine translation (SMT), aiming at increased efficiency of decoding by minimizing the number of language model computations and hypothesis expansions. Our results show that language model based pre-sorting yields a small improvement in translation quality and a speedup by a factor of 2. Two look-ahead methods are shown to further increase translation speed by a factor of 2 without changing the search space and a factor of 4 with the side-effect of some additional search errors. We compare our approach with Moses and observe the same performance, but a substantially better trade-off between translation quality and speed. At a speed of roughly 70 words per second, Moses reaches 17.2% BLEU, whereas our approach yields 20.0% with identical models. © 2012 Association for Computational Linguistics.

关键词： Decoding

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A multimodal crowdsourcing framework for transcribing historical handwritten documents 16

A multimodal crowdsourcing framework for transcribing histor...

引用

16th ACM Symposium on Document Engineering, DocEng 2016

作者： Granell, Emilio Martínez-Hinarejos, Carlos D. Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino Vera s/n 46022 Valencia Spain

ISBN: (纸本)9781450344388

Transcription of handwritten historical documents is one of the main topics in document analysis systems, due to cultural reasons. State-of-the-art handwritten text recognition systems allow to speed up the transcription task. Currently, this automatic transcription is far from perfect, and human expert revision is required in order to obtain the actual transcription. In this context, crowdsourcing emerged as a powerful tool for massive transcription at a relatively low cost, since the supervision effort of professional transcribers may be dramatically reduced. However, current transcription crowdsourcing platforms are mainly limited to the use of nonmobile devices, since the use of keyboards in mobile devices is not friendly enough for most users. This work presents the alternative of using speech dictation of handwritten text lines as transcription source in a crowdsourcing platform. The experiments explore how an initial handwritten text recognition hypothesis can be improved by using the contribution of speech recognition from several speakers, providing as a final result a better hypothesis to be amended by a professional transcriber with less effort.

关键词： Speech recognition

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Are very large N-best lists useful for SMT?

Are very large N-best lists useful for SMT?

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2007 human language technology Conference of the North American Chapter of the Association of Computational Linguistics, NAACL-HLT 2007

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

This paper describes an efficient method to extract large n-best lists from a word graph produced by a statistical machine translation system. The extraction is based on the k shortest paths algorithm which is efficient even for very large k. We show that, although we can generate large amounts of distinct translation hypotheses, these numerous candidates are not able to significantly improve overall system performance. We conclude that large n-best lists would benefit from better discriminating models. © 2007 Association for Computational Linguistics.

关键词： Machine translation

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Efficient phrase-table representation for machine translation with applications to online MT and speech translation

Efficient phrase-table representation for machine translatio...

引用

human language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2007

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

In phrase-based statistical machine translation, the phrase-table requires a large amount of memory. We will present an efficient representation with two key properties: on-demand loading and a prefix tree structure for the source phrases. We will show that this representation scales well to large data tasks and that we are able to store hundreds of millions of phrase pairs in the phrase-table. For the large Chinese-English NIST task, the memory requirements of the phrase-table are reduced to less than 20MB using the new representation with no loss in translation quality and speed. Additionally, the new representation is not limited to a specific test set, which is important for online or real-time machine translation. One problem in speech translation is the matching of phrases in the input word graph and the phrase-table. We will describe a novel algorithm that effectively solves this combinatorial problem exploiting the prefix tree data structure of the phrase-table. This algorithm enables the use of significantly larger input word graphs in a more efficient way resulting in improved translation quality. © 2007 Association for Computational Linguistics.

关键词： Machine translation

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The RWTH Machine Translation System for IWSLT 2008 5

The RWTH Machine Translation System for IWSLT 2008

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5th International Workshop on Spoken language Translation, IWSLT 2008

作者： Vilar, David Stein, Daniel Zhang, Yuqi Matusov, Evgeny Mauser, Arne Bender, Oliver Mansour, Saab Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany

RWTH's system for the 2008 IWSLT evaluation consists of a combination of different phrase-based and hierarchical statistical machine translation systems. We participated in the translation tasks for the Chinese-to-English and Arabic-to-English language pairs. We investigated different preprocessing techniques, reordering methods for the phrase-based system, including reordering of speech lattices, and syntax-based enhancements for the hierarchical systems. We also tried the combination of the Arabic-to-English and Chinese-to-English outputs as an additional submission. © 2008 International Workshop on Spoken language Translation, IWSLT 2008. All rights reserved.

关键词： Hierarchical systems

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Towards Unsupervised Learning for Handwriting recognition

Towards Unsupervised Learning for Handwriting Recognition

引用

International Workshop on Frontiers in Handwriting recognition

作者： Michal Kozielski Malte Nuhn Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We present a method for training an off-line handwriting recognition system in an unsupervised manner. For an isolated word recognition task, we are able to bootstrap the system without any annotated data. We then retrain the system using the best hypothesis from a previous recognition pass in an iterative fashion. Our approach relies only on a prior language model and does not depend on an explicit segmentation of words into characters. The resulting system shows a promising performance on a standard dataset in comparison to a system trained in a supervised fashion for the same amount of training data.

关键词： Hidden Markov models Training Vocabulary Error analysis Handwriting recognition Training data Ciphers

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