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Towards a Better Evaluation of Metrics for Machine Translation 5

Towards a Better Evaluation of Metrics for Machine Translati...

引用

5th Conference on Machine Translation, WMT 2020

作者： Stanchev, Peter Wang, Weiyue Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany

ISBN: (纸本)9781948087810

An important aspect of machine translation is its evaluation, which can be achieved through the use of a variety of metrics. To compare these metrics, the workshop on statistical machine translation annually evaluates metrics based on their correlation with human judgement. Over the years, methods for measuring correlation with humans have changed, but little research has been performed on what the optimal methods for acquiring human scores are and how human correlation can be measured. In this work, the methods for evaluating metrics at both system- and segment-level are analyzed in detail and their shortcomings are pointed out. © 2020 Association for Computational Linguistics

关键词： Machine translation

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Extending statistical machine translation with discriminative and trigger-based lexicon models

Extending statistical machine translation with discriminativ...

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2009 Conference on Empirical Methods in Natural language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009

作者： Mauser, Arne Hasan, SǍa Ney, Hermann Human Language Technology and Pattern Recognition Group Department of Computer Science 6 RWTH Aachen University Germany

In this work, we propose two extensions of standard word lexicons in statistical machine translation: A discriminative word lexicon that uses sentence-level source information to predict the target words and a trigger-based lexicon model that extends IBM model 1 with a second trigger, allowing for a more fine-grained lexical choice of target words. The models capture dependencies that go beyond the scope of conventional SMT models such as phraseand language models. We show that the models improve translation quality by 1% in BLEU over a competitive baseline on a large-scale task. © 2009 ACL and AFNLP.

关键词： computer aided language translation

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Hybrid language models using mixed types of sub-lexical units for open vocabulary German LVCSR

Hybrid language models using mixed types of sub-lexical unit...

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12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011

作者： Ali Basha Shaik, M. El-Desoky Mousa, Amr Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

German is a highly inflected language with a large number of words derived from the same root. It makes use of a high degree of word compounding leading to high Out-of-vocabulary (OOV) rates, and language Model (LM) perplexities. For such languages the use of sub-lexical units for Large Vocabulary Continuous Speech recognition (LVCSR) becomes a natural choice. In this paper, we investigate the use of mixed types of sub-lexical units in the same recognition lexicon. Namely, morphemic or syllabic units combined with pronunciations called graphones, normal graphemic morphemes or syllables along with full-words. This mixture of units is used for building hybrid LMs suitable for open vocabulary LVCSR where the system operates over an open, constantly changing vocabulary like in broadcast news, political debates, etc. A relative reduction of around 5.0% in Word Error Rate (WER) is obtained compared to a traditional full-words system. Moreover, around 40% of the OOVs are recognized. Copyright © 2011 ISCA.

关键词： Computational linguistics

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Morpheme based Factored language Models for German LVCSR

Morpheme based Factored Language Models for German LVCSR

引用

12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011

作者： El-Desoky Mousa, Amr Ali Basha Shaik, M. Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

German is a highly inflectional language, where a large number of words can be generated from the same root. It makes a liberal use of compounding leading to high Out-of-vocabulary (OOV) rates, and poor language Model (LM) probability estimates. Therefore, the use of morphemes for language modeling is considered a better choice for Large Vocabulary Continuous Speech recognition (LVCSR) than the full-words. Thereby, better lexical coverage and less LM perplexities are achieved. On the other side, the use of Factored language Models (FLMs) is considered a successful approach that allows the integration of many information sources to get better LM probability estimates. In this paper, we try a combined methodology for language modeling where both morphological decomposition and factored language modeling are used in one model called morpheme based FLM. Finally, we obtain around 2.5% relative reduction in Word Error Rate (WER) with respect to a traditional full-words system. Copyright © 2011 ISCA.

关键词： Modeling languages

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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

2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017

作者： Doetsch, Patrick Zeyer, Albert Voigtlaender, Paul Kulikov, Ilia Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52062 Germany

ISBN: (纸本)9781509041176

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. © 2017 IEEE.

关键词： lstm multi-GPU recurrent neural networks rnn software package speech recognition

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Acoustic feature combination for robust speech recognition

Acoustic feature combination for robust speech recognition

引用

2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Zolnay, András Schlüter, Ralf Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)0780388747

In this paper, we consider the use of multiple acoustic features of the speech signal for robust speech recognition. We investigate the combination of various auditory based (Mel Frequency Cepstrum Coefficients, Perceptual Linear Prediction, etc.) and articulatory based (voicedness) features. Features are combined by a Linear Discriminant Analysis based and by a log-linear model combination based techniques. We describe the two feature combination techniques and compare the experimental results. Experiments performed on the large-vocabulary task VerbMobil II (German conversational speech) show that the accuracy of automatic speech recognition systems can be improved by the combination of different acoustic features. © 2005 IEEE.

关键词： Feature extraction

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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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The RWTH Aachen Speech recognition and Machine Translation System for IWSLT 2012 9

The RWTH Aachen Speech Recognition and Machine Translation S...

引用

9th International Workshop on Spoken language Translation, IWSLT 2012

作者： Peitz, Stephan Mansour, Saab Freitag, Markus Feng, Minwei Huck, Matthias Wuebker, Joern Nuhn, Malte Nußbaum-Thom, Markus Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

In this paper, the automatic speech recognition (ASR) and statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2012 are presented. We participated in the ASR (English), MT (English-French, Arabic-English, Chinese-English, German-English) and SLT (English-French) tracks. For the MT track both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated in the MT and SLT tracks, including domain adaptation via data selection, translation model interpolation, phrase training for hierarchical and phrase-based systems, additional reordering model, word class language model, various Arabic and Chinese segmentation methods, postprocessing of speech recognition output with an SMT system, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems. © IWSLT 2012. All rights reserved.

关键词： Speech recognition

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The RWTH Aachen Machine Translation System for IWSLT 2011 8

The RWTH Aachen Machine Translation System for IWSLT 2011

引用

8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Wuebker, Joern Huck, Matthias Mansour, Saab Freitag, Markus Feng, Minwei Peitz, Stephan Schmidt, Christoph Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

In this paper the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2011 is presented. We participated in the MT (English-French, Arabic-English, Chinese-English) and SLT (English-French) tracks. Both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated, including domain adaptation via monolingual and bilingual data selection, phrase training, different lexical smoothing methods, additional reordering models for the hierarchical system, various Arabic and Chinese segmentation methods, punctuation prediction for speech recognition output, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems. © IWSLT 2011. All rights reserved.

关键词： Hierarchical systems

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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

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