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检索条件"机构=Human Language Technology and Pattern Recognition"

共 383 条记录，以下是141-150 订阅

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Investigation of Transformer-based Latent Attention Models for Neural Machine Translation 14

Investigation of Transformer-based Latent Attention Models f...

引用

14th Conference of the Association for Machine Translation in the Americas, AMTA 2020

作者： Bahar, Parnia Makarov, Nikita Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

Current neural translation networks are based on an effective attention mechanism that can be considered as an implicit probabilistic notion of alignment. Such architectures do not guarantee a high quality alignment, even though alignments can easily be used for explainable machine translation. This work describes a latent variable attention model using the transformer architecture, where we carry out an approximate marginalization over alignments. We show that the alignment quality in transformer models can be improved by introducing a latent variable for the alignments. To study the effect of the latent model, we quantitatively and qualitatively analyze the extracted alignments from the multi-head attention. We demonstrate that this method slightly improves translation quality on four WMT 2018 shared translation tasks, as well as generating more focused alignments for better interpretability. © 2020 AMTA 2020 - 14th Conference of the Association for Machine Translation in the Americas, Proceedings. All rights reserved.

关键词： Alignment

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Discriminative reordering models for statistical machine translation

Discriminative reordering models for statistical machine tra...

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2006 Workshop on Statistical Machine Translation, WMT 2006, collocated with the HLT-NAACL 2006

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

We present discriminative reordering models for phrase-based statistical machine translation. The models are trained using the maximum entropy principle. We use several types of features: based on words, based on word classes, based on the local context. We evaluate the overall performance of the reordering models as well as the contribution of the individual feature types on a word-aligned corpus. Additionally, we show improved translation performance using these reordering models compared to a state-of-the-art baseline system. © HLT-NAACL *** right reserved.

关键词： Computer aided language translation

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Translation modeling with bidirectional recurrent neural networks

Translation modeling with bidirectional recurrent neural net...

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2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Sundermeyer, Martin Alkhouli, Tamer Wuebker, Joern Ney, Hermann Human Language Technology Pattern Recognition Group RWTH Aachen University Aachen Germany Spoken Language Processing Group Univ. Paris-Sud France LIMSI/CNRS Orsay France

ISBN: (纸本)9781937284961

This work presents two different translation models using recurrent neural networks. The first one is a word-based approach using word alignments. Second, we present phrase-based translation models that are more consistent with phrasebased decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of machine translation, allowing us to use the full source sentence in our models, which is also of theoretical interest. We demonstrate that our translation models are capable of improving strong baselines already including recurrent neural language models on three tasks: IWSLT 2013 German!English, BOLT Arabic!English and Chinese!English. We obtain gains up to 1.6% BLEU and 1.7% TER by rescoring 1000-best lists. © 2014 Association for Computational Linguistics.

关键词： Recurrent neural networks

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The RWTH Phrase-based Statistical Machine Translation System 2

The RWTH Phrase-based Statistical Machine Translation System

引用

2nd International Workshop on Spoken language Translation, IWSLT 2005

作者： Zens, Richard Bender, Oliver Hasan, Saša Khadivi, Shahram Matusov, Evgeny Xu, Jia Zhang, Yuqi Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science Department RWTH Aachen University AachenD-52056 Germany

We give an overview of the RWTH phrase-based statistical machine translation system that was used in the evaluation campaign of the International Workshop on Spoken language Translation 2005. We use a two pass approach. In the first pass, we generate a list of the N best translation candidates. The second pass consists of rescoring and reranking this N-best list. We will give a description of the search algorithm as well as the models that are used in each pass. We participated in the supplied data tracks for manual transcriptions for the following translation directions: Arabic-English, Chinese-English, English-Chinese and Japanese-English. For Japanese-English, we also participated in the C-Star track. In addition, we performed translations of automatic speech recognition output for Chinese-English and Japanese-English. For both language pairs, we translated the single-best ASR hypotheses. Additionally, we translated Chinese ASR lattices. © 2005 International Workshop on Spoken language Translation, IWSLT 2005.

关键词： Speech recognition

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Chunk-level reordering of source language sentences with automatically learned rules for statistical machine translation

Chunk-level reordering of source language sentences with aut...

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2007 AMTA Workshop on Syntax and Structure in Statistical Translation, SSST 2007 at the 2007 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2007

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

In this paper, we describe a source-side reordering method based on syntactic chunks for phrase-based statistical machine translation. First, we shallow parse the source language sentences. Then, reordering rules are automatically learned from source-side chunks and word alignments. During translation, the rules are used to generate a reordering lattice for each sentence. Experimental results are reported for a Chinese-to-English task, showing an improvement of 0.5%-1.8% BLEU score absolute on various test sets and better computational efficiency than reordering during decoding. The experiments also show that the reordering at the chunk-level performs better than at the POS-level. © 2007 Association for Computational Linguistics

关键词： Computational efficiency

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Robust Knowledge Distillation from RNN-T Models with Noisy Training Labels Using Full-Sum Loss 48

Robust Knowledge Distillation from RNN-T Models with Noisy T...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zeineldeen, Mohammad Audhkhasi, Kartik Baskar, Murali Karthick Ramabhadran, Bhuvana Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany Google Llc New York United States

ISBN: (纸本)9781728163277

This work studies knowledge distillation (KD) and addresses its constraints for recurrent neural network transducer (RNN-T) models. In hard distillation, a teacher model transcribes large amounts of unlabelled speech to train a student model. Soft distillation is another popular KD method that distills the output logits of the teacher model. Due to the nature of RNN-T alignments, applying soft distillation between RNNT architectures having different posterior distributions is challenging. In addition, bad teachers having high word-error-rate (WER) reduce the efficacy of KD. We investigate how to effectively distill knowledge from variable quality ASR teachers, which has not been studied before to the best of our knowledge. We show that a sequence-level KD, full-sum distillation, outperforms other distillation methods for RNN-T models, especially for bad teachers. We also propose a variant of full-sum distillation that distills the sequence discriminative knowledge of the teacher leading to further improvement in WER. We conduct experiments on public datasets namely SpeechStew and LibriSpeech, and on in-house production data. © 2023 IEEE.

关键词： Recurrent neural networks

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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...

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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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Cipher type detection

Cipher type detection

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2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Nuhn, Malte Knight, Kevin Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Information Sciences Institute University of Southern California United States

ISBN: (纸本)9781937284961

Manual analysis and decryption of enciphered documents is a tedious and error prone work. Often-even after spending large amounts of time on a particular cipher-no decipherment can be found. Automating the decryption of various types of ciphers makes it possible to sift through the large number of encrypted messages found in libraries and archives, and to focus human effort only on a small but potentially interesting subset of them. In this work, we train a classifier that is able to predict which encipherment method has been used to generate a given ciphertext. We are able to distinguish 50 different cipher types (specified by the American Cryptogram Association) with an accuracy of 58.5%. This is a 11.2% absolute improvement over the best previously published classifier. © 2014 Association for Computational Linguistics.

关键词： Cryptography

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Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions 40

Unsupervised adaptation of a denoising autoencoder by Bayesi...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Heymann, Jahn Haeb-Umbach, Reinhold Golik, Pavel Schluter, Ralf University of Paderborn Department of Communications Engineering Paderborn Germany RWTH Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen Aachen Germany

ISBN: (纸本)9781467369978

The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter setting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data. © 2015 IEEE.

关键词： deep neuronal networks denoising autoencoder feature enhancement robust speech recognition

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Bag-of-visual-words models for adult image classification and filtering

Bag-of-visual-words models for adult image classification an...

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作者： Deselaers, Thomas Pimenidis, Lexi Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Security and Privacy Research RWTH Aachen University Aachen Germany

ISBN: (纸本)9781424421756

We present a method to classify images into different categories of pornographic content to create a system for filtering pornographic images from network traffic. Although different systems for this application were presented in the past, most of these systems are based on simple skin colour features and have rather poor performance. Recent advances in the image recognition field in particular for the classification of objects have shown that bag-of-visual-words-approaches are a good method for many image classification problems. The system we present here, is based on this approach, uses a task-specific visual vocabulary and is trained and evaluated on an image database of 8500 images from different categories. It is shown that it clearly outperforms earlier systems on this dataset and further evaluation on two novel web-traffic collections shows the good performance of the proposed system. © 2008 IEEE.

关键词： Image classification

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