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Pivot lightly-supervised training for statistical machine translation 10

Pivot lightly-supervised training for statistical machine tr...

引用

10th Conference of the Association for Machine Translation in the Americas, AMTA 2012

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

In this paper, we investigate large-scale lightly-supervised training with a pivot language: We augment a baseline statistical machine translation (SMT) system that has been trained on human-generated parallel training corpora with large amounts of additional unsupervised parallel data;but instead of creating this synthetic data from monolingual source language data with the baseline system itself, or from target language data with a reverse system, we employ a parallel corpus of target language data and data in a pivot language. The pivot language data is automatically translated into the source language, resulting in a trilingual corpus with unsupervised source language side. We augment our baseline system with the unsupervised sourcetarget parallel data. Experiments are conducted for the German- French language pair using the standard WMT newstest sets for development and testing. We obtain the unsupervised data by translating the English side of the English-French 109 corpus to German. With careful system design, we are able to achieve improvements of up to +0.4 points BLEU / -0.7 points TER over the baseline.

关键词： Computer aided language translation

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Leave-One-Out Phrase Model Training for Large-Scale Deployment 7

Leave-One-Out Phrase Model Training for Large-Scale Deployme...

引用

7th Workshop on Statistical Machine Translation, WMT 2012, immediately following the Conference of the North-American Chapter of the Association for Computational Linguistics - human language Technologies, NAACL HLT 2012

作者： Wuebker, Joern Hwang, Mei-Yuh Quirk, Chris Human Language Technology And Pattern Recognition Group Rwth Aachen University Germany Microsoft Corporation RedmondWA United States

ISBN: (纸本)9781937284206

Training the phrase table by force-aligning (FA) the training data with the reference translation has been shown to improve the phrasal translation quality while significantly reducing the phrase table size on medium sized tasks. We apply this procedure to several large-scale tasks, with the primary goal of reducing model sizes without sacrificing translation quality. To deal with the noise in the automatically crawled parallel training data, we introduce on-demand word deletions, insertions, and backoffs to achieve over 99% successful alignment rate. We also add heuristics to avoid any increase in OOV rates. We are able to reduce already heavily pruned baseline phrase tables by more than 50% with little to no degradation in quality and occasionally slight improvement, without any increase in OOVs. We further introduce two global scaling factors for re-estimation of the phrase table via posterior phrase alignment probabilities and a modified absolute discounting method that can be applied to fractional counts. ©2012 Association for Computational Linguistics.

关键词： Computational linguistics

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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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Conditional leaving-one-out and cross-validation for discount estimation in Kneser-Ney-like extensions

Conditional leaving-one-out and cross-validation for discoun...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： J. Andrés-Ferrer M. Sundermeyer H. Ney Pattern Recognition and Human Language Technology Universidad Politécnica de Valencia Spain Human Language Technology and Pattern Recognition RWTH Aachen University Germany

The smoothing of n-gram models is a core technique in language modelling (LM). Modified Kneser-Ney (mKN) ranges among one of the best smoothing techniques. This technique discounts a fixed quantity from the observed counts in order to approximate the Turing-Good (TG) counts. Despite the TG counts optimise the leaving-one-out (L1O) criterion, the discounting parameters introduced in mKN do not. Moreover, the approximation to the TG counts for large counts is heavily simplified. In this work, both ideas are addressed: the estimation of the discounting parameters by L1O and better functional forms to approximate larger TG counts. The L1O performance is compared with cross-validation (CV) and mKN baseline in two large vocabulary tasks.

关键词： Smoothing methods Approximation methods Standards Optimization Estimation Computational modeling Training

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Semantic cohesion model for phrase-based SMT

Semantic cohesion model for phrase-based SMT

引用

24th International Conference on Computational Linguistics, COLING 2012

作者： Feng, Minwei Sun, Weiwei Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany MOE Key Laboratory of Computational Linguistics Institute of Computer Science and Technology Peking University Beijing China

In this paper, we propose a novel semantic cohesion model. Our model utilizes the predicateargument structures as soft constraints and plays the role as a reordering model in the phrasebased statistical machine translation system. We build a translation system with GALE data. Experimental results on the NIST02, NIST03, NIST04, NIST05 and NIST08 Chinese-English tasks show that our model improves the baseline system by 0.93 BLEU 0.98 TER on average. We also compare our method with a syntax-augmented model (Cherry, 2008), and demonstrate the importance of predicate-argument semantics in machine translation. © 2012 The COLING.

关键词： Semantics

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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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Phrase Model Training for Statistical Machine Translation with Word Lattices of Preprocessing Alternatives 12

Phrase Model Training for Statistical Machine Translation wi...

引用

Workshop on Statistical Machine Translation

作者： Joern Wuebker Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781622765928

In statistical machine translation, word lattices are used to represent the ambiguities in the preprocessing of the source sentence, such as word segmentation for Chinese or morphological analysis for German. Several approaches have been proposed to define the probability of different paths through the lattice with external tools like word segmenters, or by applying indicator features. We introduce a novel lattice design, which explicitly distinguishes between different preprocessing alternatives for the source sentence. It allows us to make use of specific features for each preprocessing type and to lexicalize the choice of lattice path directly in the phrase translation model. We argue that forced alignment training can be used to learn lattice path and phrase translation model simultaneously. On the news-commentary portion of the German→English WMT 2011 task we can show moderate improvements of up to 0.6% Bleu over a state-of-the-art baseline system.

关键词： machine translation crystal lattices Word Pretreatment

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Analysis of Preprocessing Techniques for Latin Handwriting Recognition

Analysis of Preprocessing Techniques for Latin Handwriting R...

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International Workshop on Frontiers in Handwriting Recognition

作者： Hendrik Pesch Mahdi Hamdani Jens Forster Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

In this work we analyze the contribution of preprocessing steps for Latin handwriting recognition. A preprocessing pipeline based on geometric heuristics and image statistics is used. This pipeline is applied to French and English handwriting recognition in an HMM based framework. Results show that preprocessing improves recognition performance for the two tasks. The Maximum Likelihood (ML)-trained HMM system reaches a competitive WER of 16.7% and outperforms many sophisticated systems for the French handwriting recognition task. The results for English handwriting are comparable to other ML-trained HMM recognizers. Using MLP preprocessing a WER of 35.3% is achieved.

关键词： Hidden Markov models Databases Handwriting recognition Pipelines Text recognition Noise

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Investigations on the use of morpheme level features in language Models for Arabic LVCSR

Investigations on the use of morpheme level features in Lang...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Amr El-Desoky Mousa Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition-Computer Science Department RWTH Aachen University Aachen Germany

A major challenge for Arabic Large Vocabulary Continuous Speech Recognition (LVCSR) is the rich morphology of Arabic, which leads to high Out-of-vocabulary (OOV) rates, and poor language Model (LM) probabilities. In such cases, the use of morphemes rather than full-words is considered a better choice for LMs. Thereby, higher lexical coverage and less LM perplexities are achieved. On the other side, an effective way to increase the robustness of LMs is to incorporate features of words into LMs. In this paper, we investigate the use of features derived for morphemes rather than words. Thus, we combine the benefits of both morpheme level and feature rich modeling. We compare the performance of stream-based, class-based and Factored LMs (FLMs) estimated over sequences of morphemes and their features for performing Arabic LVCSR. A relative reduction of 3.9% in Word Error Rate (WER) is achieved compared to a word-based system.

关键词： Computational modeling Mathematical model Lattices humans Speech recognition USA Councils Interpolation

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Comparison and combination of different CRBE based MLP features for LVCSR

Comparison and combination of different CRBE based MLP featu...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zoltán Tüske Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

Multi Layer Perceptron (MLP) features extracted from different types of critical band energies (CRBE) - derived from MFCC, GT, and PLP pipeline - are compared on French broadcast news and conversational speech recognition task. Though the MLP structure is kept fixed, ROVER combination of different CRBE based systems leads to 4% relative improvement. Furthermore, aiming at the combination of state-of-the-art features based on various signal analysis methods into one single stream, posterior feature space based combination technique is proposed. The speaker normalized features originated from different CRBEs are merged after additional MLP training by Dempster-Shafer rule. The performance of these posterior features unifying the different CRBE based features is superior to the best single CRBE based posterior features by 6% relative. Further results reveal that the concatenated cepstral and unified posterior features perform nearly as well as the ROVER combination of the different CRBE based systems.

关键词： Feature extraction Mel frequency cepstral coefficient Speech Training Hidden Markov models

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