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Alignment-Based Neural Machine Translation 1

Alignment-Based Neural Machine Translation

引用

1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016

作者： Alkhouli, Tamer Bretschner, Gabriel Peter, Jan-Thorsten Hethnawi, Mohammed Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781945626104

Neural machine translation (NMT) has emerged recently as a promising statistical machine translation approach. In NMT, neural networks (NN) are directly used to produce translations, without relying on a pre-existing translation framework. In this work, we take a step towards bridging the gap between conventional word alignment models and NMT. We follow the hidden Markov model (HMM) approach that separates the alignment and lexical models. We propose a neural alignment model and combine it with a lexical neural model in a log-linear framework. The models are used in a standalone word-based decoder that explicitly hypothesizes alignments during search. We demonstrate that our system outperforms attention-based NMT on two tasks: IWSLT 2013 German→English and BOLT Chinese→English. We also show promising results for re-aligning the training data using neural models. © 2016 Association for Computational Linguistics

关键词： Neural machine translation

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Returnn: The rwth extensible training framework for universal recurrent neural networks

Returnn: The RWTH extensible training framework for universa...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Patrick Doetsch Albert Zeyer Paul Voigtlaender Ilia Kulikov Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52062 Germany

ISBN: (纸本)9781509041183

In this work we release our extensible and easily configurable neural network training software. It provides a rich set of functional layers with a particular focus on efficient training of recurrent neural network topologies on multiple GPUs. The source of the software package is public and freely available for academic research purposes and can be used as a framework or as a standalone tool which supports a flexible configuration. The software allows to train state-of-the-art deep bidirectional long short-term memory (LSTM) models on both one dimensional data like speech or two dimensional data like handwritten text and was used to develop successful submission systems in several evaluation campaigns.

关键词： recurrent neural networks lstm rnn speech recognition software package multi-gpu recurrent neural nets Software packages Speech recognition EXPANDABLE research purposes Frameworks Functional training

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Faster sequence training

Faster sequence training

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Albert Zeyer Ilia Kulikov Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52062 Germany

ISBN: (纸本)9781509041183

It has been shown that sequence-discriminative training can improve the performance for large vocabulary continuous speech recognition. Our main contribution is a novel method for reducing the computation time of any sort of sequence training while only slightly decreasing the overall performance. The method allows to parallelize the forward propagation through the network, the loss and loss gradient calculation which will provide a frame-wise error signal, and an independent forward and back propagation using that error signal. That last step can be calculated in a frame-wise manner and thus allows to use frame chunking to further improve the runtime. The loss calculation can itself be parallelized over many sequences. In addition to several experiments which outline the runtime gains, we also provide a convergence proof sketch. We extend on the research of sequence training of bidirectional long-short term memory ((B)LSTM) networks and provide an overview and comparison over different criteria. We have published all the code as part of our RETURNN and RASR framework including our training setup configurations.

关键词： sequence training LSTM computation time parallelization computational time parallelization Functional training Runtime forward propagation Training Fasting

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A comprehensive study of deep bidirectional LSTM RNNS for acoustic modeling in speech recognition

A comprehensive study of deep bidirectional LSTM RNNS for ac...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Albert Zeyer Patrick Doetsch Paul Voigtlaender Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52062 Germany

ISBN: (纸本)9781509041183

Recent experiments show that deep bidirectional long short-term memory (BLSTM) recurrent neural network acoustic models outperform feedforward neural networks for automatic speech recognition (ASR). However, their training requires a lot of tuning and experience. In this work, we provide a comprehensive overview over various BLSTM training aspects and their interplay within ASR, which has been missing so far in the literature. We investigate on different variants of optimization methods, batching, truncated backpropagation, and regularization techniques such as dropout, and we study the effect of size and depth, training models of up to 10 layers. This includes a comparison of computation times vs. recognition performance. Furthermore, we introduce a pretraining scheme for LSTMs with layer-wise construction of the network showing good improvements especially for deep networks. The experimental analysis mainly was performed on the Quaero task, with additional results on Switchboard. The best BLSTM model gave a relative improvement in word error rate of over 15% compared to our best feed-forward baseline on our Quaero 50h task. All experiments were done using RETURNN and RASR, rwth's extensible training framework for universal recurrent neural networks and ASR toolkit. The training configuration files are publicly available.

关键词： acoustic modeling LSTM RNN acoustic modeling Speech recognition recurrent neural nets aquifer storage and recovery Functional training feedforward neural nets Bidirectional

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A Comparative Study on Vocabulary Reduction for Phrase Table Smoothing 1

A Comparative Study on Vocabulary Reduction for Phrase Table...

引用

1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016

作者： Kim, Yunsu Guta, Andreas Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Lilt Inc

ISBN: (纸本)9781945626104

This work systematically analyzes the smoothing effect of vocabulary reduction for phrase translation models. We extensively compare various word-level vocabularies to show that the performance of smoothing is not significantly affected by the choice of vocabulary. This result provides empirical evidence that the standard phrase translation model is extremely sparse. Our experiments also reveal that vocabulary reduction is more effective for smoothing large-scale phrase tables. © 2016 Association for Computational Linguistics

关键词： Translation (languages)

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The rwth aachen university English-Romanian Machine Translation System for WMT 2016 1

The RWTH Aachen University English-Romanian Machine Translat...

引用

1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016

作者： Peter, Jan-Thorsten Alkhouli, Tamer Guta, Andreas Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781945626104

This paper describes the statistical machine translation system developed at rwth aachen university for the English→Romanian translation task of the ACL 2016 First Conference on Machine Translation (WMT 2016). We combined three different state-of the- art systems in a system combination: A phrase-based system, a hierarchical phrase-based system and an attentionbased neural machine translation system. The phrase-based and the hierarchical phrase-based systems make use of a language model trained on all available data, a language model trained on the bilingual data and a word class language model. In addition, we utilized a recurrent neural network language model and a bidirectional recurrent neural network translation model for reranking the output of both systems. The attention-based neural machine translation system was trained using all bilingual data together with the backtranslated data from the News Crawl 2015 corpora. © 2016 Association for Computational Linguistics.

关键词： Neural machine translation

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CharacTER: Translation Edit Rate on Character Level 1

CharacTER: Translation Edit Rate on Character Level

引用

1st Conference on Machine Translation, WMT 2016, held at the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016

作者： Wang, Weiyue Peter, Jan-Thorsten Rosendahl, Hendrik Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany

ISBN: (纸本)9781945626104

Recently, the capability of character-level evaluation measures for machine translation output has been confirmed by several metrics. This work proposes translation edit rate on character level (CharacTER), which calculates the character level edit distance while performing the shift edit on word level. The novel metric shows high system-level correlation with human rankings, especially for morphologically rich languages. It outperforms the strong CHRF by up to 7% correlation on different metric tasks. In addition, we apply the hypothesis sentence length for normalizing the edit distance in CharacTER, which also provides significant improvements compared to using the reference sentence length. © 2016 Association for Computational Linguistics.

关键词： Computational linguistics

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Deep sign: Hybrid CNN-HMM for continuous sign language recognition 27

Deep sign: Hybrid CNN-HMM for continuous sign language recog...

引用

27th British Machine Vision Conference, BMVC 2016

作者： Koller, Oscar Zargaran, Sepehr Ney, Hermann Bowden, Richard Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

This paper introduces the end-to-end embedding of a CNN into a HMM, while interpreting the outputs of the CNN in a Bayesian fashion. The hybrid CNN-HMM combines the strong discriminative abilities of CNNs with the sequence modelling capabilities of HMMs. Most current approaches in the field of gesture and sign language recognition disregard the necessity of dealing with sequence data both for training and evaluation. With our presented end-to-end embedding we are able to improve over the state-of-the-art on three challenging benchmark continuous sign language recognition tasks by between 15% and 38% relative and up to 13.3% absolute. © 2016. The copyright of this document resides with its authors.

关键词： Embeddings

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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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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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