检索结果-内蒙古大学图书馆

2013 Conference on Empirical Methods in Natural language Processing, EMNLP 2013

作者： Wuebker, Joern Peitz, Stephan Rietig, Felix Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781937284978

Automatically clustering words from a monolingual or bilingual training corpus into classes is a widely used technique in statistical natural language processing. We present a very simple and easy to implement method for using these word classes to improve translation quality. It can be applied across different machine translation paradigms and with arbitrary types of models. We show its efficacy on a small German → English and a larger French → German translation task with both standard phrase-based and hierarchical phrase-based translation systems for a common set of models. Our results show that with word class models, the baseline can be improved by up to 1.4% BLEU and 1.0% TER on the French → German task and 0.3% BLEU and 1.1% TER on the German → English task. © 2013 Association for Computational Linguistics.

关键词： Machine translation

来源：评论

学校读者我要写书评

暂无评论

A comparison between count and neural network models based on joint translation and reordering sequences

A comparison between count and neural network models based o...

引用

Conference on Empirical Methods in Natural language Processing, EMNLP 2015

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

ISBN: (纸本)9781941643327

We propose a conversion of bilingual sentence pairs and the corresponding word alignments into novel linear sequences. These are joint translation and reordering (JTR) uniquely defined sequences, combining interdepending lexical and alignment dependencies on the word level into a single framework. They are constructed in a simple manner while capturing multiple alignments and empty words. JTR sequences can be used to train a variety of models. We investigate the performances of ngram models with modified Kneser-Ney smoothing, feed-forward and recurrent neural network architectures when estimated on JTR sequences, and compare them to the operation sequence model (Durrani et al., 2013b). Evaluations on the IWSLT German→English, WMT German→English and BOLT Chinese→English tasks show that JTR models improve state-of-the-art phrasebased systems by up to 2.2 BLEU. © 2015 Association for Computational Linguistics.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

Investigations on phrase-based decoding with recurrent neural network language and translation models 10

Investigations on phrase-based decoding with recurrent neura...

引用

10th Workshop on Statistical Machine Translation, WMT 2015 at the 2015 Conference on Empirical Methods in Natural language Processing, EMNLP 2015

作者： Alkhouli, Tamer Rietig, Felix Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University Aachen Germany

ISBN: (纸本)9781941643327

This work explores the application of recurrent neural network (RNN) language and translation models during phrasebased decoding. Due to their use of unbounded context, the decoder integration of RNNs is more challenging compared to the integration of feedforward neural models. In this paper, we apply approximations and use caching to enable RNN decoder integration, while requiring reasonable memory and time resources. We analyze the effect of caching on translation quality and speed, and use it to integrate RNN language and translation models into a phrase-based decoder. To the best of our knowledge, no previous work has discussed the integration of RNN translation models into phrase-based decoding. We also show that a special RNN can be integrated efficiently without the need for approximations. We compare decoding using RNNs to rescoring n-best lists on two tasks: IWSLT 2013 German→English, and BOLT Arabic→English. We demonstrate that the performance of decoding with RNNs is at least as good as using them in rescoring. © EMNLP 2015. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep fisher faces 28

Deep fisher faces

引用

28th British Machine Vision Conference, BMVC 2017

作者： Hanselmann, Harald Yan, Shen Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)190172560X

Most current state-of-the-art methods for unconstrained face recognition use deep convolutional neural networks. Recently, it has been proposed to augment the typically used softmax cross-entropy loss by adding a center loss trying to minimize the distance between the face images and their class centers. In this work we further extend the center (intra-class) loss with an inter-class loss reminiscent of the popular early face recognition approach Fisherfaces. To this end we add a term that directly optimizes the distances of the class centers appearing in a batch in dependence of the input images. We evaluate the new loss on two popular databases for unconstrained face recognition, the Labeled Faces in the Wild and the Youtube Faces database. In both cases the new loss achieves competitive results. © 2017. The copyright of this document resides with its authors.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

When and why is document-level context useful in neural machine translation? 4

When and why is document-level context useful in neural mach...

引用

4th Workshop on Discourse in Machine Translation, DiscoMT@EMNLP 2019

作者： Kim, Yunsu Tran, Duc Thanh Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

ISBN: (纸本)9781950737741

Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT. © 2019 Association for Computational Linguistics.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

When and Why is Unsupervised Neural Machine Translation Useless? 22

When and Why is Unsupervised Neural Machine Translation Usel...

引用

22nd Annual Conference of the European Association for Machine Translation, EAMT 2020

作者： Kim, Yunsu Graça, Miguel Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University Aachen Germany

ISBN: (纸本)9789893305898

This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT). In ten translation tasks with various data settings, we analyze the conditions under which the unsupervised methods fail to produce reasonable translations. We show that their performance is severely affected by linguistic dissimilarity and domain mismatch between source and target monolingual data. Such conditions are common for low-resource language pairs, where unsupervised learning works poorly. In all of our experiments, supervised and semi-supervised baselines with 50k-sentence bilingual data outperform the best unsupervised results. Our analyses pinpoint the limits of the current unsupervised NMT and also suggest immediate research directions. © 2020 The authors.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

FIRE in ImageCLEF 2007

FIRE in ImageCLEF 2007

引用

2007 Cross language Evaluation Forum Workshop, CLEF 2007, co-located with the 11th European Conference on Digital Libraries, ECDL 2007

作者： Deselaers, Thomas Gass, Tobias Weyand, Tobias Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We present the methods we applied in the four different tasks of the ImageCLEF 2007 content-based image retrieval evaluation. We participated in all four tasks using a variety of methods. Global and local image descriptors are applied using nearest neighbour search for the medical and photo retrieval tasks and discriminative models for the object retrieval and the medical automatic annotation task. For the photo and medical retrieval task, we apply a maximum entropy training method to learn an optimal feature weighting from the queries and qrels from last year. This method works particularly well if the queries are very similar as they were in the medical retrieval task.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

Lexicon Models for Hierarchical Phrase-Based Machine Translation 8

Lexicon Models for Hierarchical Phrase-Based Machine Transla...

引用

8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Huck, Matthias Mansour, Saab Wiesler, Simon Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

In this paper, we investigate lexicon models for hierarchical phrase-based statistical machine translation. We study five types of lexicon models: a model which is extracted from word-aligned training data and-given the word alignment matrix-relies on pure relative frequencies [1];the IBM model 1 lexicon [2];a regularized version of IBM model 1;a triplet lexicon model variant [3];and a discriminatively trained word lexicon model [4]. We explore source-to-target models with phrase-level as well as sentence-level scoring and target-to-source models with scoring on phrase level only. For the first two types of lexicon models, we compare several scoring variants. All models are used during search, i.e. they are incorporated directly into the log-linear model combination of the decoder. Phrase table smoothing with triplet lexicon models and with discriminative word lexicons are novel contributions. We also propose a new regularization technique for IBM model 1 by means of the Kullback-Leibler divergence with the empirical unigram distribution as regularization term. Experiments are carried out on the large-scale NIST Chinese→English translation task and on the English→French and Arabic→English IWSLT TED tasks. For Chinese→English and English→French, we obtain the best results by using the discriminative word lexicon to smooth our phrase tables. © IWSLT 2011. All rights reserved.

关键词： Computer aided language translation

来源：评论

学校读者我要写书评

暂无评论

Tracking benchmark databases for video-based sign language recognition

Tracking benchmark databases for video-based sign language r...

引用

11th European Conference on Computer Vision, ECCV 2010

作者： Dreuw, Philippe Forster, Jens Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

A survey of video databases that can be used within a continuous sign language recognition scenario to measure the performance of head and hand tracking algorithms either w.r.t. a tracking error rate or w.r.t. a word error rate criterion is presented in this work. Robust tracking algorithms are required as the signing hand frequently moves in front of the face, may temporarily disappear, or cross the other hand. Only few studies consider the recognition of continuous sign language, and usually special devices such as colored gloves or blue-boxing environments are used to accurately track the regions-of-interest in sign language processing. Ground-truth labels for hand and head positions have been annotated for more than 30k frames in several publicly available video databases of different degrees of difficulty, and preliminary tracking results are presented. © 2012 Springer-Verlag.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

Spoken language processing techniques for sign language recognition and translation

引用

technology and Disability 2008年第2期20卷 121-133页

作者： Dreuw, Philippe Stein, Daniel Deselaers, Thomas Rybach, David Zahedi, Morteza Bungeroth, Jan Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department 6 RWTH Aachen University Aachen Germany

We present an approach to automatically recognize sign language and translate it into a spoken language. A system to address these tasks is created based on state-of-the-art techniques from statistical machine translation, speech recognition, and image processing research. Such a system is necessary for communication between deaf and hearing people. The communication is otherwise nearly impossible due to missing sign language skills on the hearing side, and the low reading and writing skills on the deaf side. As opposed to most current approaches, which focus on the recognition of isolated signs only, we present a system that recognizes complete sentences in sign language. Similar to speech recognition, we have to deal with temporal sequences. Instead of the acoustic signal in speech recognition, we process a video signal as input. Therefore, we use a speech recognition system to obtain a textual representation of the signed sentences. This intermediate representation is then fed into a statistical machine translation system to create a translation into a spoken language. To achieve good results, some particularities of sign languages are considered in both systems. We use a publicly available corpus to show the performance of the proposed system and report very promising results. © 2008 IOS Press. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：