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

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

引用

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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Improving Continuous Sign language Recognition: Speech Recognition Techniques and System Design 4

Improving Continuous Sign Language Recognition: Speech Recog...

引用

4th Workshop on Speech and language Processing for Assistive Technologies, SLPAT 2013

作者： Forster, Jens Koller, Oscar Oberdorfer, Christian Gweth, Yannick Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284930

Automatic sign language recognition (ASLR) is a special case of automatic speech recognition (ASR) and computer vision (CV) and is currently evolving from using artificial labgenerated data to using 'real-life' data. Although ASLR still struggles with feature extraction, it can benefit from techniques developed for ASR. We present a large-vocabulary ASLR system that is able to recognize sentences in continuous sign language and uses features extracted from standard single-view video cameras without using additional equipment. ASR techniques such as the multi-layer-perceptron (MLP) tandem approach, speaker adaptation, pronunciation modelling, and parallel hidden Markov models are investigated. We evaluate the influence of each system component on the recognition performance. On two publicly available large vocabulary databases representing lab-data (25 signer, 455 sign vocabulary, 19k sentence) and unconstrained 'real-life' sign language (1 signer, 266 sign vocabulary, 351 sentences) we can achieve 22.1% respectively 38.6% WER. © 2013 Association for Computational Linguistics.

关键词： Vocabulary control

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Non-Stationary Signal Processing and its Application in Speech Recognition

Non-Stationary Signal Processing and its Application in Spee...

引用

2012 SAPA-SCALE Conference

作者： Tüske, Zoltán Drepper, Friedhelm R. Schlüter, Ralf Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany

The most widely used acoustic feature extraction methods of current automatic speech recognition (ASR) systems are based on the assumption of stationarity. In this paper we extensively evaluate a recently introduced filter stable, non-stationary signal processing method, which relies on an adaptive part-tone decomposition of voiced speech to obtain alternative feature vectors for ASR. The non-stationary filterbank allows for more noise robust amplitude based features by suppressing the between-harmonics regions. Furthermore, by adapting the center filter frequencies to the underlying acoustic modes, it is possible to obtain useful phase features which can be interpreted in terms of the non-stationary dynamics within the vocal tract. The features are evaluated on different tasks ranging from vowel classification up to large vocabulary continuous speech recognition. © SAPA-SCALE 2012. All rights reserved.

关键词： Adaptive filters

来源：评论

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Extraction methods of voicing feature for robust speech recognition 8

Extraction methods of voicing feature for robust speech reco...

引用

8th European Conference on Speech Communication and technology, EUROSPEECH 2003

作者： Zolnay, András Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Department of Computer Science VI RWTH Aachen - University of Technology Aachen52056 Germany

In this paper, three different voicing features are studied as additional acoustic features for continuous speech recognition. The harmonic product spectrum based feature is extracted in frequency domain while the autocorrelation and the average magnitude difference based methods work in time domain. The algorithms produce a measure of voicing for each time frame. The voicing measure was combined with the standard Mel Frequency Cepstral Coefficients (MFCC) using linear discriminant analysis to choose the most relevant features. Experiments have been performed on small and large vocabulary tasks. The three different voicing measures combined with MFCCs resulted in similar improvements in word error rate: improvements of up to 14% on the small-vocabulary task and improvements of up to 6% on the large-vocabulary task relative to using MFCC alone with the same overall number of parameters in the system.

关键词： Frequency domain analysis

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Advancements in Arabic-to-English hierarchical machine translation

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

引用

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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Interactive-Predictive Neural Multimodal Systems 9th

Interactive-Predictive Neural Multimodal Systems

引用

9th Iberian Conference on pattern Recognition and Image Analysis, IbPRIA 2019

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

ISBN: (纸本)9783030313319

Despite the advances achieved by neural models in sequence to sequence learning, exploited in a variety of tasks, they still make errors. In many use cases, these are corrected by a human expert in a posterior revision process. The interactive-predictive framework aims to minimize the human effort spent on this process by considering partial corrections for iteratively refining the hypothesis. In this work, we generalize the interactive-predictive approach, typically applied in to machine translation field, to tackle other multimodal problems namely, image and video captioning. We study the application of this framework to multimodal neural sequence to sequence models. We show that, following this framework, we approximately halve the effort spent for correcting the outputs generated by the automatic systems. Moreover, we deploy our systems in a publicly accessible demonstration, that allows to better understand the behavior of the interactive-predictive framework. © 2019, Springer Nature Switzerland AG.

关键词： pattern recognition

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Interactive-predictive translation based on multiple word-segments 19

Interactive-predictive translation based on multiple word-se...

引用

19th Annual Conference of the European Association for Machine Translation, EAMT 2016

作者： Domingo, Miguel Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Camino de Vera s/n Valencia46022 Spain

Current machine translation systems require human revision to produce high-quality translations. This is achieved through a post-editing process or by means of an interactive human-computer collaboration. Most protocols belonging to the last scenario follow a left-to-right strategy, where the prefix of the translation is iteratively increased by successive validations and corrections made by the user. In this work, we propose a new interactive protocol which allows the user to validate all correct word sequences in the translation generated by the system, breaking the left-to-right barrier. We evaluated our proposal through simulated experiments, obtaining large reductions of the human effort. © 2016, European Association for Machine Translation. All rights reserved.

关键词： Machine translation

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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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Statistical machine translation and its challenges 8

Statistical machine translation and its challenges

引用

8th International Conference on Spoken language Processing, ICSLP 2004

作者： Ney, Hermann Lehrstuhl für Informatik VI Human Language Technology and Pattern Recognition RWTH Aachen - University of Technology AachenD-52056 Germany

In addition to speech recognition and syntactic parsing, during the last 10 years, the statistical approach has found widespread use in machine translation of both written language and spoken language. In many comparative evaluations, the statistical approach was found to be competitive or superior to the existing conventional approaches. Since the first statistical approach was proposed at the end of the 80s, many attempts have been made to improve the state of the art. Like other natural language processing tasks, machine translation requires four major components: a decision rule, a set of probability models, a training criterion and an efficient generation of the target sentence. We will consider each of these four components in more detail and point out promising research directions.

关键词： Machine translation

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The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018 3

The RWTH Aachen University English-German and German-English...

引用

3rd Conference on Machine Translation, WMT 2018 at the Conference on Empirical Methods in Natural language Processing, EMNLP 2018

作者： Graça, Miguel Kim, Yunsu Schamper, Julian Geng, Jiahui Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

ISBN: (纸本)9781948087810

This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer. ©2018 Association for Computational Linguistics

关键词： Neural machine translation

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